Overview

Dataset statistics

Number of variables51
Number of observations587960
Missing cells8506122
Missing cells (%)28.4%
Duplicate rows5627
Duplicate rows (%)1.0%
Total size in memory1.2 GiB
Average record size in memory2.1 KiB

Variable types

Categorical29
Numeric19
Unsupported3

Alerts

SG_UF has constant value "RJ"Constant
Dataset has 5627 (1.0%) duplicate rowsDuplicates
DT_PRESTACAO_CONTAS has a high cardinality: 810 distinct valuesHigh cardinality
NM_UE has a high cardinality: 92 distinct valuesHigh cardinality
NM_CANDIDATO has a high cardinality: 15007 distinct valuesHigh cardinality
DS_CNAE_FORNECEDOR has a high cardinality: 319 distinct valuesHigh cardinality
NM_FORNECEDOR has a high cardinality: 251160 distinct valuesHigh cardinality
NM_FORNECEDOR_RFB has a high cardinality: 231292 distinct valuesHigh cardinality
NM_MUNICIPIO_FORNECEDOR has a high cardinality: 62 distinct valuesHigh cardinality
NR_DOCUMENTO has a high cardinality: 96749 distinct valuesHigh cardinality
DT_DESPESA has a high cardinality: 494 distinct valuesHigh cardinality
DS_DESPESA has a high cardinality: 192977 distinct valuesHigh cardinality
CD_ELEICAO is highly overall correlated with ANO_ELEICAO and 15 other fieldsHigh correlation
SQ_PRESTADOR_CONTAS is highly overall correlated with ANO_ELEICAO and 17 other fieldsHigh correlation
NR_CNPJ_PRESTADOR_CONTA is highly overall correlated with ANO_ELEICAO and 18 other fieldsHigh correlation
CD_CARGO is highly overall correlated with ANO_ELEICAO and 20 other fieldsHigh correlation
SQ_CANDIDATO is highly overall correlated with ANO_ELEICAO and 19 other fieldsHigh correlation
NR_CANDIDATO is highly overall correlated with CD_CARGO and 9 other fieldsHigh correlation
NR_CPF_CANDIDATO is highly overall correlated with NM_UE and 8 other fieldsHigh correlation
NR_CPF_VICE_CANDIDATO is highly overall correlated with ANO_ELEICAO and 16 other fieldsHigh correlation
NR_PARTIDO is highly overall correlated with NM_UE and 9 other fieldsHigh correlation
CD_CNAE_FORNECEDOR is highly overall correlated with NM_UE and 3 other fieldsHigh correlation
NR_CPF_CNPJ_FORNECEDOR is highly overall correlated with CD_TIPO_FORNECEDOR and 6 other fieldsHigh correlation
CD_MUNICIPIO_FORNECEDOR is highly overall correlated with NM_UE and 2 other fieldsHigh correlation
SQ_CANDIDATO_FORNECEDOR is highly overall correlated with SG_UF_FORNECEDOR and 2 other fieldsHigh correlation
NR_CANDIDATO_FORNECEDOR is highly overall correlated with NM_UE and 14 other fieldsHigh correlation
CD_CARGO_FORNECEDOR is highly overall correlated with ANO_ELEICAO and 20 other fieldsHigh correlation
NR_PARTIDO_FORNECEDOR is highly overall correlated with NM_UE and 10 other fieldsHigh correlation
CD_ORIGEM_DESPESA is highly overall correlated with CD_TIPO_FORNECEDOR and 5 other fieldsHigh correlation
SQ_DESPESA is highly overall correlated with ANO_ELEICAO and 16 other fieldsHigh correlation
VR_DESPESA_CONTRATADA is highly overall correlated with DS_CARGO_FORNECEDORHigh correlation
ANO_ELEICAO is highly overall correlated with CD_ELEICAO and 16 other fieldsHigh correlation
CD_TIPO_ELEICAO is highly overall correlated with NM_TIPO_ELEICAO and 7 other fieldsHigh correlation
NM_TIPO_ELEICAO is highly overall correlated with CD_TIPO_ELEICAO and 7 other fieldsHigh correlation
DS_ELEICAO is highly overall correlated with ANO_ELEICAO and 17 other fieldsHigh correlation
DT_ELEICAO is highly overall correlated with ANO_ELEICAO and 17 other fieldsHigh correlation
TP_PRESTACAO_CONTAS is highly overall correlated with NM_MUNICIPIO_FORNECEDOR and 2 other fieldsHigh correlation
NM_UE is highly overall correlated with ANO_ELEICAO and 26 other fieldsHigh correlation
DS_CARGO is highly overall correlated with ANO_ELEICAO and 20 other fieldsHigh correlation
SG_PARTIDO is highly overall correlated with ANO_ELEICAO and 21 other fieldsHigh correlation
NM_PARTIDO is highly overall correlated with ANO_ELEICAO and 21 other fieldsHigh correlation
CD_TIPO_FORNECEDOR is highly overall correlated with DS_TIPO_FORNECEDOR and 4 other fieldsHigh correlation
DS_TIPO_FORNECEDOR is highly overall correlated with CD_TIPO_FORNECEDOR and 4 other fieldsHigh correlation
SG_UF_FORNECEDOR is highly overall correlated with NM_UE and 5 other fieldsHigh correlation
NM_MUNICIPIO_FORNECEDOR is highly overall correlated with ST_TURNO and 24 other fieldsHigh correlation
DS_CARGO_FORNECEDOR is highly overall correlated with ANO_ELEICAO and 19 other fieldsHigh correlation
SG_PARTIDO_FORNECEDOR is highly overall correlated with ANO_ELEICAO and 24 other fieldsHigh correlation
NM_PARTIDO_FORNECEDOR is highly overall correlated with ANO_ELEICAO and 24 other fieldsHigh correlation
DS_TIPO_DOCUMENTO is highly overall correlated with CD_TIPO_FORNECEDOR and 6 other fieldsHigh correlation
DS_ORIGEM_DESPESA is highly overall correlated with CD_TIPO_FORNECEDOR and 6 other fieldsHigh correlation
ST_TURNO is highly overall correlated with CD_CARGO and 3 other fieldsHigh correlation
NR_CPF_VICE_CANDIDATO has 458110 (77.9%) missing valuesMissing
CD_CNAE_FORNECEDOR has 349188 (59.4%) missing valuesMissing
DS_CNAE_FORNECEDOR has 349188 (59.4%) missing valuesMissing
NR_CPF_CNPJ_FORNECEDOR has 61023 (10.4%) missing valuesMissing
NM_FORNECEDOR has 61023 (10.4%) missing valuesMissing
NM_FORNECEDOR_RFB has 61598 (10.5%) missing valuesMissing
CD_ESFERA_PART_FORNECEDOR has 587960 (100.0%) missing valuesMissing
DS_ESFERA_PART_FORNECEDOR has 587960 (100.0%) missing valuesMissing
SG_UF_FORNECEDOR has 585869 (99.6%) missing valuesMissing
CD_MUNICIPIO_FORNECEDOR has 586933 (99.8%) missing valuesMissing
NM_MUNICIPIO_FORNECEDOR has 586933 (99.8%) missing valuesMissing
SQ_CANDIDATO_FORNECEDOR has 586217 (99.7%) missing valuesMissing
NR_CANDIDATO_FORNECEDOR has 586190 (99.7%) missing valuesMissing
CD_CARGO_FORNECEDOR has 586190 (99.7%) missing valuesMissing
DS_CARGO_FORNECEDOR has 586190 (99.7%) missing valuesMissing
NR_PARTIDO_FORNECEDOR has 585876 (99.6%) missing valuesMissing
SG_PARTIDO_FORNECEDOR has 585876 (99.6%) missing valuesMissing
NM_PARTIDO_FORNECEDOR has 585876 (99.6%) missing valuesMissing
DS_TIPO_DOCUMENTO has 62938 (10.7%) missing valuesMissing
NR_DOCUMENTO has 63079 (10.7%) missing valuesMissing
VR_DESPESA_CONTRATADA is highly skewed (γ1 = 195.0530155)Skewed
SG_UE is an unsupported type, check if it needs cleaning or further analysisUnsupported
CD_ESFERA_PART_FORNECEDOR is an unsupported type, check if it needs cleaning or further analysisUnsupported
DS_ESFERA_PART_FORNECEDOR is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2022-12-06 16:05:25.392337
Analysis finished2022-12-06 16:12:18.096109
Duration6 minutes and 52.7 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

ANO_ELEICAO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.2 MiB
2020
232964 
2022
192800 
2018
162196 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2351840
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2020 232964
39.6%
2022 192800
32.8%
2018 162196
27.6%

Length

2022-12-06T13:12:18.338589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:18.482783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 232964
39.6%
2022 192800
32.8%
2018 162196
27.6%

Most occurring characters

ValueCountFrequency (%)
2 1206524
51.3%
0 820924
34.9%
1 162196
 
6.9%
8 162196
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2351840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1206524
51.3%
0 820924
34.9%
1 162196
 
6.9%
8 162196
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2351840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1206524
51.3%
0 820924
34.9%
1 162196
 
6.9%
8 162196
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2351840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1206524
51.3%
0 820924
34.9%
1 162196
 
6.9%
8 162196
 
6.9%

CD_TIPO_ELEICAO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
2
585692 
1
 
2268

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters587960
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 585692
99.6%
1 2268
 
0.4%

Length

2022-12-06T13:12:18.557925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:18.671943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 585692
99.6%
1 2268
 
0.4%

Most occurring characters

ValueCountFrequency (%)
2 585692
99.6%
1 2268
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 587960
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 585692
99.6%
1 2268
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 587960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 585692
99.6%
1 2268
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 587960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 585692
99.6%
1 2268
 
0.4%

NM_TIPO_ELEICAO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.4 MiB
Ordinária
585692 
Suplementar
 
2268

Length

Max length11
Median length9
Mean length9.0077148
Min length9

Characters and Unicode

Total characters5296176
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrdinária
2nd rowOrdinária
3rd rowOrdinária
4th rowOrdinária
5th rowOrdinária

Common Values

ValueCountFrequency (%)
Ordinária 585692
99.6%
Suplementar 2268
 
0.4%

Length

2022-12-06T13:12:18.761529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:18.881939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ordinária 585692
99.6%
suplementar 2268
 
0.4%

Most occurring characters

ValueCountFrequency (%)
r 1173652
22.2%
i 1171384
22.1%
n 587960
11.1%
a 587960
11.1%
O 585692
11.1%
d 585692
11.1%
á 585692
11.1%
e 4536
 
0.1%
S 2268
 
< 0.1%
u 2268
 
< 0.1%
Other values (4) 9072
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4708216
88.9%
Uppercase Letter 587960
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1173652
24.9%
i 1171384
24.9%
n 587960
12.5%
a 587960
12.5%
d 585692
12.4%
á 585692
12.4%
e 4536
 
0.1%
u 2268
 
< 0.1%
p 2268
 
< 0.1%
l 2268
 
< 0.1%
Other values (2) 4536
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
O 585692
99.6%
S 2268
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5296176
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1173652
22.2%
i 1171384
22.1%
n 587960
11.1%
a 587960
11.1%
O 585692
11.1%
d 585692
11.1%
á 585692
11.1%
e 4536
 
0.1%
S 2268
 
< 0.1%
u 2268
 
< 0.1%
Other values (4) 9072
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4710484
88.9%
None 585692
 
11.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1173652
24.9%
i 1171384
24.9%
n 587960
12.5%
a 587960
12.5%
O 585692
12.4%
d 585692
12.4%
e 4536
 
0.1%
S 2268
 
< 0.1%
u 2268
 
< 0.1%
p 2268
 
< 0.1%
Other values (3) 6804
 
0.1%
None
ValueCountFrequency (%)
á 585692
100.0%

CD_ELEICAO
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean430.11278
Minimum297
Maximum551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:18.954624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum297
5-th percentile297
Q1297
median426
Q3546
95-th percentile546
Maximum551
Range254
Interquartile range (IQR)249

Descriptive statistics

Standard deviation96.596428
Coefficient of variation (CV)0.22458395
Kurtosis-1.321733
Mean430.11278
Median Absolute Deviation (MAD)120
Skewness-0.15197093
Sum2.5288911 × 108
Variance9330.8699
MonotonicityNot monotonic
2022-12-06T13:12:19.051482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
426 230696
39.2%
546 192800
32.8%
297 162196
27.6%
551 767
 
0.1%
508 529
 
0.1%
506 279
 
< 0.1%
507 265
 
< 0.1%
459 190
 
< 0.1%
458 123
 
< 0.1%
532 115
 
< 0.1%
ValueCountFrequency (%)
297 162196
27.6%
426 230696
39.2%
458 123
 
< 0.1%
459 190
 
< 0.1%
506 279
 
< 0.1%
507 265
 
< 0.1%
508 529
 
0.1%
532 115
 
< 0.1%
546 192800
32.8%
551 767
 
0.1%
ValueCountFrequency (%)
551 767
 
0.1%
546 192800
32.8%
532 115
 
< 0.1%
508 529
 
0.1%
507 265
 
< 0.1%
506 279
 
< 0.1%
459 190
 
< 0.1%
458 123
 
< 0.1%
426 230696
39.2%
297 162196
27.6%

DS_ELEICAO
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.9 MiB
Eleições Municipais 2020
230696 
Eleições Gerais Estaduais 2022
192800 
Eleições Gerais Estaduais 2018
162196 
RJ - Suplementar de Itatiaia
 
1032
RJ - Suplementar de Silva Jardim
 
529
Other values (3)
 
707

Length

Max length37
Median length30
Mean length27.649267
Min length24

Characters and Unicode

Total characters16256663
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEleições Gerais Estaduais 2018
2nd rowEleições Gerais Estaduais 2018
3rd rowEleições Gerais Estaduais 2018
4th rowEleições Gerais Estaduais 2018
5th rowEleições Gerais Estaduais 2018

Common Values

ValueCountFrequency (%)
Eleições Municipais 2020 230696
39.2%
Eleições Gerais Estaduais 2022 192800
32.8%
Eleições Gerais Estaduais 2018 162196
27.6%
RJ - Suplementar de Itatiaia 1032
 
0.2%
RJ - Suplementar de Silva Jardim 529
 
0.1%
RJ - Suplementar de Sta. Ma. Madalena 469
 
0.1%
RJ - Suplementar de Itatiaia 123
 
< 0.1%
RJ - Suplementar de Carapebus 115
 
< 0.1%

Length

2022-12-06T13:12:19.156531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:19.288026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
eleições 585692
27.6%
gerais 354996
16.7%
estaduais 354996
16.7%
municipais 230696
 
10.9%
2020 230696
 
10.9%
2022 192800
 
9.1%
2018 162196
 
7.6%
de 2268
 
0.1%
suplementar 2268
 
0.1%
2268
 
0.1%
Other values (8) 6003
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i 1991140
12.2%
s 1881491
11.6%
1537042
 
9.5%
e 1533768
 
9.4%
a 1305050
 
8.0%
2 1201988
 
7.4%
E 940688
 
5.8%
0 816388
 
5.0%
l 588958
 
3.6%
u 588075
 
3.6%
Other values (22) 3872075
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10836728
66.7%
Decimal Number 2342768
 
14.4%
Space Separator 1537042
 
9.5%
Uppercase Letter 1536919
 
9.5%
Dash Punctuation 2268
 
< 0.1%
Other Punctuation 938
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1991140
18.4%
s 1881491
17.4%
e 1533768
14.2%
a 1305050
12.0%
l 588958
 
5.4%
u 588075
 
5.4%
ç 585692
 
5.4%
õ 585692
 
5.4%
t 360043
 
3.3%
d 358262
 
3.3%
Other values (7) 1058557
9.8%
Uppercase Letter
ValueCountFrequency (%)
E 940688
61.2%
G 354996
 
23.1%
M 231634
 
15.1%
S 3266
 
0.2%
J 2797
 
0.2%
R 2268
 
0.1%
I 1155
 
0.1%
C 115
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 1201988
51.3%
0 816388
34.8%
1 162196
 
6.9%
8 162196
 
6.9%
Space Separator
ValueCountFrequency (%)
1537042
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2268
100.0%
Other Punctuation
ValueCountFrequency (%)
. 938
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12373647
76.1%
Common 3883016
 
23.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1991140
16.1%
s 1881491
15.2%
e 1533768
12.4%
a 1305050
10.5%
E 940688
7.6%
l 588958
 
4.8%
u 588075
 
4.8%
ç 585692
 
4.7%
õ 585692
 
4.7%
t 360043
 
2.9%
Other values (15) 2013050
16.3%
Common
ValueCountFrequency (%)
1537042
39.6%
2 1201988
31.0%
0 816388
21.0%
1 162196
 
4.2%
8 162196
 
4.2%
- 2268
 
0.1%
. 938
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15085279
92.8%
None 1171384
 
7.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1991140
13.2%
s 1881491
12.5%
1537042
10.2%
e 1533768
10.2%
a 1305050
8.7%
2 1201988
8.0%
E 940688
 
6.2%
0 816388
 
5.4%
l 588958
 
3.9%
u 588075
 
3.9%
Other values (20) 2700691
17.9%
None
ValueCountFrequency (%)
ç 585692
50.0%
õ 585692
50.0%

DT_ELEICAO
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.6 MiB
15/11/2020
230696 
02/10/2022
192800 
07/10/2018
162196 
12/09/2021
 
1073
13/03/2022
 
767
Other values (2)
 
428

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5879600
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row07/10/2018
2nd row07/10/2018
3rd row07/10/2018
4th row07/10/2018
5th row07/10/2018

Common Values

ValueCountFrequency (%)
15/11/2020 230696
39.2%
02/10/2022 192800
32.8%
07/10/2018 162196
27.6%
12/09/2021 1073
 
0.2%
13/03/2022 767
 
0.1%
11/04/2021 313
 
0.1%
07/11/2021 115
 
< 0.1%

Length

2022-12-06T13:12:19.406069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:19.525868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
15/11/2020 230696
39.2%
02/10/2022 192800
32.8%
07/10/2018 162196
27.6%
12/09/2021 1073
 
0.2%
13/03/2022 767
 
0.1%
11/04/2021 313
 
0.1%
07/11/2021 115
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1530916
26.0%
2 1401164
23.8%
1 1213477
20.6%
/ 1175920
20.0%
5 230696
 
3.9%
7 162311
 
2.8%
8 162196
 
2.8%
3 1534
 
< 0.1%
9 1073
 
< 0.1%
4 313
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4703680
80.0%
Other Punctuation 1175920
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1530916
32.5%
2 1401164
29.8%
1 1213477
25.8%
5 230696
 
4.9%
7 162311
 
3.5%
8 162196
 
3.4%
3 1534
 
< 0.1%
9 1073
 
< 0.1%
4 313
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 1175920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5879600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1530916
26.0%
2 1401164
23.8%
1 1213477
20.6%
/ 1175920
20.0%
5 230696
 
3.9%
7 162311
 
2.8%
8 162196
 
2.8%
3 1534
 
< 0.1%
9 1073
 
< 0.1%
4 313
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5879600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1530916
26.0%
2 1401164
23.8%
1 1213477
20.6%
/ 1175920
20.0%
5 230696
 
3.9%
7 162311
 
2.8%
8 162196
 
2.8%
3 1534
 
< 0.1%
9 1073
 
< 0.1%
4 313
 
< 0.1%

ST_TURNO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
1
571189 
2
 
16771

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters587960
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 571189
97.1%
2 16771
 
2.9%

Length

2022-12-06T13:12:19.637766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:19.737891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 571189
97.1%
2 16771
 
2.9%

Most occurring characters

ValueCountFrequency (%)
1 571189
97.1%
2 16771
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 587960
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 571189
97.1%
2 16771
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 587960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 571189
97.1%
2 16771
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 587960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 571189
97.1%
2 16771
 
2.9%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.8 MiB
Final
583283 
Parcial
 
3562
Relatório Financeiro
 
1022
Regularização da Omissão
 
93

Length

Max length24
Median length5
Mean length5.041195
Min length5

Characters and Unicode

Total characters2964021
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFinal
2nd rowFinal
3rd rowFinal
4th rowFinal
5th rowFinal

Common Values

ValueCountFrequency (%)
Final 583283
99.2%
Parcial 3562
 
0.6%
Relatório Financeiro 1022
 
0.2%
Regularização da Omissão 93
 
< 0.1%

Length

2022-12-06T13:12:19.838608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:19.969692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
final 583283
99.0%
parcial 3562
 
0.6%
relatório 1022
 
0.2%
financeiro 1022
 
0.2%
regularização 93
 
< 0.1%
da 93
 
< 0.1%
omissão 93
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 592730
20.0%
i 590097
19.9%
l 587960
19.8%
n 585327
19.7%
F 584305
19.7%
r 5699
 
0.2%
c 4584
 
0.2%
P 3562
 
0.1%
o 2230
 
0.1%
e 2137
 
0.1%
Other values (13) 5390
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2373738
80.1%
Uppercase Letter 589075
 
19.9%
Space Separator 1208
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 592730
25.0%
i 590097
24.9%
l 587960
24.8%
n 585327
24.7%
r 5699
 
0.2%
c 4584
 
0.2%
o 2230
 
0.1%
e 2137
 
0.1%
ó 1022
 
< 0.1%
t 1022
 
< 0.1%
Other values (8) 930
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
F 584305
99.2%
P 3562
 
0.6%
R 1115
 
0.2%
O 93
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2962813
> 99.9%
Common 1208
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 592730
20.0%
i 590097
19.9%
l 587960
19.8%
n 585327
19.8%
F 584305
19.7%
r 5699
 
0.2%
c 4584
 
0.2%
P 3562
 
0.1%
o 2230
 
0.1%
e 2137
 
0.1%
Other values (12) 4182
 
0.1%
Common
ValueCountFrequency (%)
1208
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2962720
> 99.9%
None 1301
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 592730
20.0%
i 590097
19.9%
l 587960
19.8%
n 585327
19.8%
F 584305
19.7%
r 5699
 
0.2%
c 4584
 
0.2%
P 3562
 
0.1%
o 2230
 
0.1%
e 2137
 
0.1%
Other values (10) 4089
 
0.1%
None
ValueCountFrequency (%)
ó 1022
78.6%
ã 186
 
14.3%
ç 93
 
7.1%
Distinct810
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size37.6 MiB
01/11/2022
86990 
15/12/2020
48873 
31/10/2022
 
27810
14/12/2020
 
18197
06/11/2018
 
12643
Other values (805)
393447 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5879600
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st row05/12/2018
2nd row05/12/2018
3rd row05/12/2018
4th row05/12/2018
5th row05/12/2018

Common Values

ValueCountFrequency (%)
01/11/2022 86990
 
14.8%
15/12/2020 48873
 
8.3%
31/10/2022 27810
 
4.7%
14/12/2020 18197
 
3.1%
06/11/2018 12643
 
2.2%
11/12/2020 12361
 
2.1%
27/10/2022 9855
 
1.7%
23/11/2018 8650
 
1.5%
28/10/2022 6861
 
1.2%
26/10/2022 6713
 
1.1%
Other values (800) 349007
59.4%

Length

2022-12-06T13:12:20.092943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01/11/2022 86990
 
14.8%
15/12/2020 48873
 
8.3%
31/10/2022 27810
 
4.7%
14/12/2020 18197
 
3.1%
06/11/2018 12643
 
2.2%
11/12/2020 12361
 
2.1%
27/10/2022 9855
 
1.7%
23/11/2018 8650
 
1.5%
28/10/2022 6861
 
1.2%
26/10/2022 6713
 
1.1%
Other values (800) 349007
59.4%

Most occurring characters

ValueCountFrequency (%)
2 1589450
27.0%
1 1267619
21.6%
0 1207281
20.5%
/ 1175920
20.0%
8 144633
 
2.5%
9 123074
 
2.1%
3 102770
 
1.7%
5 88756
 
1.5%
4 73561
 
1.3%
7 59754
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4703680
80.0%
Other Punctuation 1175920
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1589450
33.8%
1 1267619
26.9%
0 1207281
25.7%
8 144633
 
3.1%
9 123074
 
2.6%
3 102770
 
2.2%
5 88756
 
1.9%
4 73561
 
1.6%
7 59754
 
1.3%
6 46782
 
1.0%
Other Punctuation
ValueCountFrequency (%)
/ 1175920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5879600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1589450
27.0%
1 1267619
21.6%
0 1207281
20.5%
/ 1175920
20.0%
8 144633
 
2.5%
9 123074
 
2.1%
3 102770
 
1.7%
5 88756
 
1.5%
4 73561
 
1.3%
7 59754
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5879600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1589450
27.0%
1 1267619
21.6%
0 1207281
20.5%
/ 1175920
20.0%
8 144633
 
2.5%
9 123074
 
2.1%
3 102770
 
1.7%
5 88756
 
1.5%
4 73561
 
1.3%
7 59754
 
1.0%

SQ_PRESTADOR_CONTAS
Real number (ℝ)

Distinct16270
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0878865 × 109
Minimum4.1626776 × 108
Maximum3.8725941 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:20.206937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4.1626776 × 108
5-th percentile4.1905182 × 108
Q14.2724936 × 108
median1.8444692 × 109
Q33.7626912 × 109
95-th percentile3.7948122 × 109
Maximum3.8725941 × 109
Range3.4563263 × 109
Interquartile range (IQR)3.3354419 × 109

Descriptive statistics

Standard deviation1.3146677 × 109
Coefficient of variation (CV)0.62966436
Kurtosis-1.3910749
Mean2.0878865 × 109
Median Absolute Deviation (MAD)1.4219704 × 109
Skewness0.13258493
Sum1.2275937 × 1015
Variance1.7283511 × 1018
MonotonicityNot monotonic
2022-12-06T13:12:20.338488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
420246859 6425
 
1.1%
3794815849 3730
 
0.6%
3786152254 3652
 
0.6%
3759262008 3084
 
0.5%
1844985336 3053
 
0.5%
416268286 2735
 
0.5%
1828967591 2681
 
0.5%
3786149039 2604
 
0.4%
1829968303 2464
 
0.4%
422524131 2464
 
0.4%
Other values (16260) 555068
94.4%
ValueCountFrequency (%)
416267760 4
 
< 0.1%
416267792 2
 
< 0.1%
416267818 18
 
< 0.1%
416267841 2
 
< 0.1%
416267842 7
 
< 0.1%
416267843 664
0.1%
416267936 161
 
< 0.1%
416267937 5
 
< 0.1%
416267956 16
 
< 0.1%
416267968 640
0.1%
ValueCountFrequency (%)
3872594057 1
 
< 0.1%
3872224213 3
 
< 0.1%
3872224194 73
< 0.1%
3872224103 55
< 0.1%
3869614225 2
 
< 0.1%
3858814809 1
 
< 0.1%
3858069456 10
 
< 0.1%
3858069430 18
 
< 0.1%
3858069422 12
 
< 0.1%
3858069407 18
 
< 0.1%

SG_UF
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.1 MiB
RJ
587960 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1175920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRJ
2nd rowRJ
3rd rowRJ
4th rowRJ
5th rowRJ

Common Values

ValueCountFrequency (%)
RJ 587960
100.0%

Length

2022-12-06T13:12:20.454386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:20.558307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
rj 587960
100.0%

Most occurring characters

ValueCountFrequency (%)
R 587960
50.0%
J 587960
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1175920
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 587960
50.0%
J 587960
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1175920
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 587960
50.0%
J 587960
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1175920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 587960
50.0%
J 587960
50.0%

SG_UE
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size28.0 MiB

NM_UE
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct92
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.5 MiB
RIO DE JANEIRO
391906 
SÃO GONÇALO
 
10065
NITERÓI
 
9323
DUQUE DE CAXIAS
 
9278
NOVA IGUAÇU
 
9074
Other values (87)
158314 

Length

Max length29
Median length14
Mean length13.146124
Min length4

Characters and Unicode

Total characters7729395
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRIO DE JANEIRO
2nd rowRIO DE JANEIRO
3rd rowRIO DE JANEIRO
4th rowRIO DE JANEIRO
5th rowRIO DE JANEIRO

Common Values

ValueCountFrequency (%)
RIO DE JANEIRO 391906
66.7%
SÃO GONÇALO 10065
 
1.7%
NITERÓI 9323
 
1.6%
DUQUE DE CAXIAS 9278
 
1.6%
NOVA IGUAÇU 9074
 
1.5%
SÃO JOÃO DE MERITI 6940
 
1.2%
CAMPOS DOS GOYTACAZES 6213
 
1.1%
ITAGUAÍ 5531
 
0.9%
QUEIMADOS 5492
 
0.9%
VOLTA REDONDA 5228
 
0.9%
Other values (82) 128910
 
21.9%

Length

2022-12-06T13:12:20.654825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 416290
26.9%
rio 399953
25.8%
janeiro 391906
25.3%
são 24637
 
1.6%
dos 12924
 
0.8%
nova 11622
 
0.8%
gonçalo 10065
 
0.6%
niterói 9323
 
0.6%
duque 9278
 
0.6%
caxias 9278
 
0.6%
Other values (124) 253851
16.4%

Most occurring characters

ValueCountFrequency (%)
O 1005406
13.0%
961167
12.4%
I 960352
12.4%
R 942991
12.2%
E 919375
11.9%
A 700879
9.1%
D 496074
6.4%
N 471271
6.1%
J 407072
5.3%
S 147827
 
1.9%
Other values (25) 716981
9.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6767792
87.6%
Space Separator 961167
 
12.4%
Dash Punctuation 436
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1005406
14.9%
I 960352
14.2%
R 942991
13.9%
E 919375
13.6%
A 700879
10.4%
D 496074
7.3%
N 471271
7.0%
J 407072
6.0%
S 147827
 
2.2%
U 82488
 
1.2%
Other values (23) 634057
9.4%
Space Separator
ValueCountFrequency (%)
961167
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 436
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6767792
87.6%
Common 961603
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1005406
14.9%
I 960352
14.2%
R 942991
13.9%
E 919375
13.6%
A 700879
10.4%
D 496074
7.3%
N 471271
7.0%
J 407072
6.0%
S 147827
 
2.2%
U 82488
 
1.2%
Other values (23) 634057
9.4%
Common
ValueCountFrequency (%)
961167
> 99.9%
- 436
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7607129
98.4%
None 122266
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1005406
13.2%
961167
12.6%
I 960352
12.6%
R 942991
12.4%
E 919375
12.1%
A 700879
9.2%
D 496074
6.5%
N 471271
6.2%
J 407072
5.4%
S 147827
 
1.9%
Other values (16) 594715
7.8%
None
ValueCountFrequency (%)
à 36672
30.0%
Ç 23647
19.3%
Ó 22491
18.4%
É 13662
 
11.2%
Í 11837
 
9.7%
Á 7225
 
5.9%
Ú 3403
 
2.8%
Ê 2481
 
2.0%
Ô 848
 
0.7%

NR_CNPJ_PRESTADOR_CONTA
Real number (ℝ)

Distinct16262
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1794235 × 1012
Minimum3.1123042 × 1013
Maximum4.7934024 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:20.808487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.1123042 × 1013
5-th percentile3.1167009 × 1013
Q13.124078 × 1013
median3.8806983 × 1013
Q34.741069 × 1013
95-th percentile4.754666 × 1013
Maximum4.7934024 × 1013
Range1.6810982 × 1013
Interquartile range (IQR)1.616991 × 1013

Descriptive statistics

Standard deviation6.3336813 × 1012
Coefficient of variation (CV)0.77434324
Kurtosis-1.3612522
Mean8.1794235 × 1012
Median Absolute Deviation (MAD)7.598401 × 1012
Skewness0.0063916588
Sum4.8091739 × 1018
Variance4.0115519 × 1025
MonotonicityNot monotonic
2022-12-06T13:12:20.941154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31192176000116 6425
 
1.1%
47546679000182 3730
 
0.6%
47508630000135 3652
 
0.6%
47392066000138 3084
 
0.5%
38843968000152 3053
 
0.5%
31123059000109 2735
 
0.5%
38516232000170 2681
 
0.5%
47508223000128 2604
 
0.4%
38533570000110 2464
 
0.4%
31214772000150 2464
 
0.4%
Other values (16252) 555068
94.4%
ValueCountFrequency (%)
31123042000143 17
 
< 0.1%
31123046000121 50
 
< 0.1%
31123047000176 2
 
< 0.1%
31123049000165 435
0.1%
31123050000190 88
 
< 0.1%
31123051000134 316
0.1%
31123053000123 43
 
< 0.1%
31123054000178 61
 
< 0.1%
31123055000112 6
 
< 0.1%
31123056000167 4
 
< 0.1%
ValueCountFrequency (%)
47934024000181 1
 
< 0.1%
47924205000127 55
< 0.1%
47924195000120 73
< 0.1%
47924186000139 3
 
< 0.1%
47906471000127 2
 
< 0.1%
47810290000100 31
< 0.1%
47798546000101 1
 
< 0.1%
47791875000112 10
 
< 0.1%
47791873000123 18
 
< 0.1%
47791872000189 18
 
< 0.1%

CD_CARGO
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5529934
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:21.062031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q16
median7
Q311
95-th percentile13
Maximum13
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9940327
Coefficient of variation (CV)0.35005671
Kurtosis-1.3610034
Mean8.5529934
Median Absolute Deviation (MAD)1
Skewness0.37225254
Sum5028818
Variance8.9642319
MonotonicityNot monotonic
2022-12-06T13:12:21.151587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 193832
33.0%
7 139488
23.7%
13 124726
21.2%
11 108238
18.4%
3 15513
 
2.6%
5 6163
 
1.0%
ValueCountFrequency (%)
3 15513
 
2.6%
5 6163
 
1.0%
6 193832
33.0%
7 139488
23.7%
11 108238
18.4%
13 124726
21.2%
ValueCountFrequency (%)
13 124726
21.2%
11 108238
18.4%
7 139488
23.7%
6 193832
33.0%
5 6163
 
1.0%
3 15513
 
2.6%

DS_CARGO
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
Deputado Federal
193832 
Deputado Estadual
139488 
Vereador
124726 
Prefeito
108238 
Governador
 
15513

Length

Max length17
Median length16
Mean length12.814802
Min length7

Characters and Unicode

Total characters7534591
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeputado Estadual
2nd rowDeputado Estadual
3rd rowDeputado Estadual
4th rowDeputado Estadual
5th rowDeputado Estadual

Common Values

ValueCountFrequency (%)
Deputado Federal 193832
33.0%
Deputado Estadual 139488
23.7%
Vereador 124726
21.2%
Prefeito 108238
18.4%
Governador 15513
 
2.6%
Senador 6163
 
1.0%

Length

2022-12-06T13:12:21.262554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:21.375636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
deputado 333320
36.2%
federal 193832
21.0%
estadual 139488
15.1%
vereador 124726
 
13.5%
prefeito 108238
 
11.7%
governador 15513
 
1.7%
senador 6163
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 1208588
16.0%
a 952530
12.6%
d 813042
10.8%
o 603473
8.0%
r 588711
7.8%
t 581046
7.7%
u 472808
 
6.3%
D 333320
 
4.4%
l 333320
 
4.4%
333320
 
4.4%
Other values (12) 1314433
17.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6279991
83.3%
Uppercase Letter 921280
 
12.2%
Space Separator 333320
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1208588
19.2%
a 952530
15.2%
d 813042
12.9%
o 603473
9.6%
r 588711
9.4%
t 581046
9.3%
u 472808
 
7.5%
l 333320
 
5.3%
p 333320
 
5.3%
s 139488
 
2.2%
Other values (4) 253665
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
D 333320
36.2%
F 193832
21.0%
E 139488
15.1%
V 124726
 
13.5%
P 108238
 
11.7%
G 15513
 
1.7%
S 6163
 
0.7%
Space Separator
ValueCountFrequency (%)
333320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7201271
95.6%
Common 333320
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1208588
16.8%
a 952530
13.2%
d 813042
11.3%
o 603473
8.4%
r 588711
8.2%
t 581046
8.1%
u 472808
 
6.6%
D 333320
 
4.6%
l 333320
 
4.6%
p 333320
 
4.6%
Other values (11) 981113
13.6%
Common
ValueCountFrequency (%)
333320
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7534591
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1208588
16.0%
a 952530
12.6%
d 813042
10.8%
o 603473
8.0%
r 588711
7.8%
t 581046
7.7%
u 472808
 
6.3%
D 333320
 
4.4%
l 333320
 
4.4%
333320
 
4.4%
Other values (12) 1314433
17.4%

SQ_CANDIDATO
Real number (ℝ)

Distinct16270
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9000105 × 1011
Minimum1.900006 × 1011
Maximum1.9000174 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:21.506807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.900006 × 1011
5-th percentile1.900006 × 1011
Q11.9000062 × 1011
median1.9000089 × 1011
Q31.900016 × 1011
95-th percentile1.9000164 × 1011
Maximum1.9000174 × 1011
Range1138171
Interquartile range (IQR)978736

Descriptive statistics

Standard deviation430105.07
Coefficient of variation (CV)2.2636984 × 10-6
Kurtosis-1.5917035
Mean1.9000105 × 1011
Median Absolute Deviation (MAD)283526
Skewness0.37387866
Sum1.1171302 × 1017
Variance1.8499037 × 1011
MonotonicityNot monotonic
2022-12-06T13:12:21.638519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190000609934 6425
 
1.1%
190001644983 3730
 
0.6%
190001619202 3652
 
0.6%
190001601720 3084
 
0.5%
190000945336 3053
 
0.5%
190000601128 2735
 
0.5%
190000640057 2681
 
0.5%
190001619517 2604
 
0.4%
190000662122 2464
 
0.4%
190000613601 2464
 
0.4%
Other values (16260) 555068
94.4%
ValueCountFrequency (%)
190000601071 205
< 0.1%
190000601072 4
 
< 0.1%
190000601073 8
 
< 0.1%
190000601074 6
 
< 0.1%
190000601075 123
< 0.1%
190000601076 19
 
< 0.1%
190000601077 15
 
< 0.1%
190000601078 156
< 0.1%
190000601079 4
 
< 0.1%
190000601081 37
 
< 0.1%
ValueCountFrequency (%)
190001739242 1
 
< 0.1%
190001738883 3
 
< 0.1%
190001738882 73
< 0.1%
190001738867 55
< 0.1%
190001737551 2
 
< 0.1%
190001732486 1
 
< 0.1%
190001732307 18
 
< 0.1%
190001732306 18
 
< 0.1%
190001732304 10
 
< 0.1%
190001732303 12
 
< 0.1%

NR_CANDIDATO
Real number (ℝ)

Distinct6355
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16330.669
Minimum10
Maximum90999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:21.785559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13
Q11177
median5540
Q323023
95-th percentile70010
Maximum90999
Range90989
Interquartile range (IQR)21846

Descriptive statistics

Standard deviation21663.388
Coefficient of variation (CV)1.3265463
Kurtosis1.5936059
Mean16330.669
Median Absolute Deviation (MAD)5525
Skewness1.5659482
Sum9.6017801 × 109
Variance4.6930238 × 108
MonotonicityNot monotonic
2022-12-06T13:12:21.938518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 15220
 
2.6%
13 11058
 
1.9%
12 10671
 
1.8%
22 10553
 
1.8%
55 9472
 
1.6%
20 9045
 
1.5%
10 7815
 
1.3%
11 7376
 
1.3%
15 7042
 
1.2%
77 5269
 
0.9%
Other values (6345) 494439
84.1%
ValueCountFrequency (%)
10 7815
1.3%
11 7376
1.3%
12 10671
1.8%
13 11058
1.9%
14 1885
 
0.3%
15 7042
1.2%
16 163
 
< 0.1%
17 4942
0.8%
18 399
 
0.1%
19 1598
 
0.3%
ValueCountFrequency (%)
90999 132
< 0.1%
90991 13
 
< 0.1%
90990 78
< 0.1%
90963 1
 
< 0.1%
90951 2
 
< 0.1%
90949 19
 
< 0.1%
90922 17
 
< 0.1%
90909 22
 
< 0.1%
90901 2
 
< 0.1%
90900 47
 
< 0.1%

NM_CANDIDATO
Categorical

Distinct15007
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size48.5 MiB
EDUARDO DA COSTA PAES
 
7366
AUREO LIDIO MOREIRA RIBEIRO
 
5197
BENEDITA SOUZA DA SILVA SAMPAIO
 
5067
MARIA LAURA MONTEZA DE SOUZA CARNEIRO
 
4738
GUTEMBERG REIS DE OLIVEIRA
 
4606
Other values (15002)
560986 

Length

Max length54
Median length47
Mean length26.139974
Min length8

Characters and Unicode

Total characters15369259
Distinct characters42
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1498 ?
Unique (%)0.3%

Sample

1st rowMARCELO NASCIF SIMÃO
2nd rowMARCELO NASCIF SIMÃO
3rd rowMARCELO NASCIF SIMÃO
4th rowMARCELO NASCIF SIMÃO
5th rowMARCELO NASCIF SIMÃO

Common Values

ValueCountFrequency (%)
EDUARDO DA COSTA PAES 7366
 
1.3%
AUREO LIDIO MOREIRA RIBEIRO 5197
 
0.9%
BENEDITA SOUZA DA SILVA SAMPAIO 5067
 
0.9%
MARIA LAURA MONTEZA DE SOUZA CARNEIRO 4738
 
0.8%
GUTEMBERG REIS DE OLIVEIRA 4606
 
0.8%
LEONARDO CARNEIRO MONTEIRO PICCIANI 4483
 
0.8%
DIMAS DE PAIVA GADELHA JUNIOR 3179
 
0.5%
ALEXANDRE AUGUSTUS SERFIOTIS 3101
 
0.5%
BENEDITA SOUZA DA SILVA 3053
 
0.5%
CHRISTINO AUREO DA SILVA 2938
 
0.5%
Other values (14997) 544232
92.6%

Length

2022-12-06T13:12:22.110499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 153011
 
6.5%
da 105621
 
4.5%
silva 90354
 
3.8%
souza 48572
 
2.1%
santos 46196
 
2.0%
oliveira 41580
 
1.8%
dos 34669
 
1.5%
luiz 30260
 
1.3%
costa 24953
 
1.1%
pereira 22515
 
1.0%
Other values (7104) 1757090
74.6%

Most occurring characters

ValueCountFrequency (%)
A 1936381
12.6%
1769250
11.5%
E 1395377
9.1%
O 1314993
 
8.6%
R 1268490
 
8.3%
I 1261538
 
8.2%
S 942423
 
6.1%
L 760655
 
4.9%
N 717017
 
4.7%
D 678700
 
4.4%
Other values (32) 3324435
21.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13600008
88.5%
Space Separator 1769250
 
11.5%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1936381
14.2%
E 1395377
10.3%
O 1314993
9.7%
R 1268490
9.3%
I 1261538
9.3%
S 942423
 
6.9%
L 760655
 
5.6%
N 717017
 
5.3%
D 678700
 
5.0%
C 457159
 
3.4%
Other values (30) 2867275
21.1%
Space Separator
ValueCountFrequency (%)
1769250
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13600008
88.5%
Common 1769251
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1936381
14.2%
E 1395377
10.3%
O 1314993
9.7%
R 1268490
9.3%
I 1261538
9.3%
S 942423
 
6.9%
L 760655
 
5.6%
N 717017
 
5.3%
D 678700
 
5.0%
C 457159
 
3.4%
Other values (30) 2867275
21.1%
Common
ValueCountFrequency (%)
1769250
> 99.9%
, 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15271653
99.4%
None 97606
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1936381
12.7%
1769250
11.6%
E 1395377
9.1%
O 1314993
8.6%
R 1268490
 
8.3%
I 1261538
 
8.3%
S 942423
 
6.2%
L 760655
 
5.0%
N 717017
 
4.7%
D 678700
 
4.4%
Other values (18) 3226829
21.1%
None
ValueCountFrequency (%)
É 27609
28.3%
à 20011
20.5%
Ç 15295
15.7%
Á 11278
11.6%
Ô 4978
 
5.1%
Ú 4532
 
4.6%
Í 4440
 
4.5%
Ê 3973
 
4.1%
Ó 2700
 
2.8%
 1524
 
1.6%
Other values (4) 1266
 
1.3%

NR_CPF_CANDIDATO
Real number (ℝ)

Distinct14729
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6636687 × 1010
Minimum3496740
Maximum9.9999448 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:22.288348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3496740
5-th percentile9.4171476 × 108
Q15.1651967 × 109
median9.7181207 × 109
Q34.7712245 × 1010
95-th percentile8.9470215 × 1010
Maximum9.9999448 × 1010
Range9.9995951 × 1010
Interquartile range (IQR)4.2547048 × 1010

Descriptive statistics

Standard deviation3.0373358 × 1010
Coefficient of variation (CV)1.1402829
Kurtosis-0.42015556
Mean2.6636687 × 1010
Median Absolute Deviation (MAD)7.3981669 × 109
Skewness1.0610219
Sum1.5661307 × 1016
Variance9.2254086 × 1020
MonotonicityNot monotonic
2022-12-06T13:12:22.456654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36293334787 8120
 
1.4%
1475189702 7366
 
1.3%
5355525725 5197
 
0.9%
79401546720 4738
 
0.8%
7733341736 4606
 
0.8%
8436066731 4483
 
0.8%
4510489706 3179
 
0.5%
2440200786 3101
 
0.5%
70542783720 2938
 
0.5%
7845799700 2825
 
0.5%
Other values (14719) 541407
92.1%
ValueCountFrequency (%)
3496740 11
 
< 0.1%
3512703 47
 
< 0.1%
4772733 90
 
< 0.1%
6616780 1
 
< 0.1%
7487738 2013
0.3%
8596727 35
 
< 0.1%
9168702 6
 
< 0.1%
9317724 7
 
< 0.1%
9714642 9
 
< 0.1%
10814701 5
 
< 0.1%
ValueCountFrequency (%)
99999447791 1
 
< 0.1%
99997339720 1
 
< 0.1%
99975688772 4
< 0.1%
99974428734 3
< 0.1%
99969114700 1
 
< 0.1%
99964635753 5
< 0.1%
99962454700 4
< 0.1%
99961920678 3
< 0.1%
99952254768 7
< 0.1%
99944103500 1
 
< 0.1%

NR_CPF_VICE_CANDIDATO
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct601
Distinct (%)0.5%
Missing458110
Missing (%)77.9%
Infinite0
Infinite (%)0.0%
Mean2.8118104 × 1010
Minimum16568729
Maximum9.9750016 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:22.627035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16568729
5-th percentile7.4809777 × 108
Q13.6448038 × 109
median1.0027261 × 1010
Q35.1538904 × 1010
95-th percentile8.7518211 × 1010
Maximum9.9750016 × 1010
Range9.9733447 × 1010
Interquartile range (IQR)4.78941 × 1010

Descriptive statistics

Standard deviation3.0536017 × 1010
Coefficient of variation (CV)1.0859913
Kurtosis-0.64449319
Mean2.8118104 × 1010
Median Absolute Deviation (MAD)8.380208 × 109
Skewness0.9004066
Sum3.6511358 × 1015
Variance9.3244832 × 1020
MonotonicityNot monotonic
2022-12-06T13:12:22.791141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45837040706.0 6425
 
1.1%
87518210797.0 3053
 
0.5%
1397326751.0 2681
 
0.5%
51538903768.0 2464
 
0.4%
9127812707.0 2464
 
0.4%
5620068773.0 2244
 
0.4%
3602776751.0 2179
 
0.4%
1891690795.0 2177
 
0.4%
483343757.0 2074
 
0.4%
3027078755.0 2005
 
0.3%
Other values (591) 102084
 
17.4%
(Missing) 458110
77.9%
ValueCountFrequency (%)
16568729.0 27
 
< 0.1%
34745777.0 10
 
< 0.1%
138178763.0 207
 
< 0.1%
160714761.0 72
 
< 0.1%
189539720.0 16
 
< 0.1%
209623713.0 68
 
< 0.1%
243984707.0 57
 
< 0.1%
249712784.0 28
 
< 0.1%
260155799.0 526
0.1%
270287710.0 11
 
< 0.1%
ValueCountFrequency (%)
99750015720.0 234
 
< 0.1%
99590638791.0 16
 
< 0.1%
99488957700.0 21
 
< 0.1%
99485370768.0 39
 
< 0.1%
99171104704.0 44
 
< 0.1%
98950622734.0 880
0.1%
98271016768.0 18
 
< 0.1%
98087436768.0 114
 
< 0.1%
97406120753.0 1283
0.2%
97192465704.0 39
 
< 0.1%

NR_PARTIDO
Real number (ℝ)

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.784004
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:22.953003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q114
median23
Q345
95-th percentile77
Maximum90
Range80
Interquartile range (IQR)31

Descriptive statistics

Standard deviation21.093067
Coefficient of variation (CV)0.66363783
Kurtosis-0.057962671
Mean31.784004
Median Absolute Deviation (MAD)11
Skewness0.97272979
Sum18687723
Variance444.91749
MonotonicityNot monotonic
2022-12-06T13:12:23.090674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
55 49216
 
8.4%
15 44949
 
7.6%
25 41941
 
7.1%
22 39496
 
6.7%
13 39001
 
6.6%
11 38733
 
6.6%
44 37575
 
6.4%
77 31312
 
5.3%
12 28540
 
4.9%
10 26354
 
4.5%
Other values (25) 210843
35.9%
ValueCountFrequency (%)
10 26354
4.5%
11 38733
6.6%
12 28540
4.9%
13 39001
6.6%
14 15944
 
2.7%
15 44949
7.6%
16 357
 
0.1%
17 17477
 
3.0%
18 3624
 
0.6%
19 11548
 
2.0%
ValueCountFrequency (%)
90 10922
 
1.9%
80 99
 
< 0.1%
77 31312
5.3%
70 13622
 
2.3%
65 6586
 
1.1%
55 49216
8.4%
54 356
 
0.1%
51 7807
 
1.3%
50 16725
 
2.8%
45 13942
 
2.4%

SG_PARTIDO
Categorical

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 MiB
PSD
49216 
MDB
44949 
DEM
41941 
PT
39001 
PP
38733 
Other values (35)
374120 

Length

Max length13
Median length12
Mean length4.1511106
Min length2

Characters and Unicode

Total characters2440687
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPP
2nd rowPP
3rd rowPP
4th rowPP
5th rowPP

Common Values

ValueCountFrequency (%)
PSD 49216
 
8.4%
MDB 44949
 
7.6%
DEM 41941
 
7.1%
PT 39001
 
6.6%
PP 38733
 
6.6%
UNIÃO 33538
 
5.7%
SOLIDARIEDADE 31312
 
5.3%
PL 29608
 
5.0%
PDT 28540
 
4.9%
REPUBLICANOS 19997
 
3.4%
Other values (30) 231125
39.3%

Length

2022-12-06T13:12:23.215435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
psd 49216
 
8.2%
mdb 44949
 
7.5%
dem 41941
 
7.0%
pt 39001
 
6.5%
pp 38733
 
6.4%
união 33538
 
5.6%
solidariedade 31312
 
5.2%
pl 29608
 
4.9%
pdt 28540
 
4.7%
republicanos 19997
 
3.3%
Other values (32) 244297
40.6%

Most occurring characters

ValueCountFrequency (%)
P 438208
18.0%
D 315930
12.9%
S 199432
 
8.2%
A 162953
 
6.7%
E 156980
 
6.4%
I 150330
 
6.2%
O 143633
 
5.9%
B 130769
 
5.4%
T 124210
 
5.1%
L 115475
 
4.7%
Other values (12) 502767
20.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2414343
98.9%
Space Separator 13172
 
0.5%
Lowercase Letter 13172
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 438208
18.2%
D 315930
13.1%
S 199432
8.3%
A 162953
 
6.7%
E 156980
 
6.5%
I 150330
 
6.2%
O 143633
 
5.9%
B 130769
 
5.4%
T 124210
 
5.1%
L 115475
 
4.8%
Other values (9) 476423
19.7%
Lowercase Letter
ValueCountFrequency (%)
d 6586
50.0%
o 6586
50.0%
Space Separator
ValueCountFrequency (%)
13172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2427515
99.5%
Common 13172
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 438208
18.1%
D 315930
13.0%
S 199432
8.2%
A 162953
 
6.7%
E 156980
 
6.5%
I 150330
 
6.2%
O 143633
 
5.9%
B 130769
 
5.4%
T 124210
 
5.1%
L 115475
 
4.8%
Other values (11) 489595
20.2%
Common
ValueCountFrequency (%)
13172
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2407149
98.6%
None 33538
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 438208
18.2%
D 315930
13.1%
S 199432
8.3%
A 162953
 
6.8%
E 156980
 
6.5%
I 150330
 
6.2%
O 143633
 
6.0%
B 130769
 
5.4%
T 124210
 
5.2%
L 115475
 
4.8%
Other values (11) 469229
19.5%
None
ValueCountFrequency (%)
à 33538
100.0%

NM_PARTIDO
Categorical

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.3 MiB
Partido Social Democrático
49216 
Movimento Democrático Brasileiro
44949 
Democratas
41941 
Partido dos Trabalhadores
39001 
UNIÃO BRASIL
 
33538
Other values (36)
379315 

Length

Max length46
Median length32
Mean length20.923755
Min length4

Characters and Unicode

Total characters12302331
Distinct characters44
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProgressistas
2nd rowProgressistas
3rd rowProgressistas
4th rowProgressistas
5th rowProgressistas

Common Values

ValueCountFrequency (%)
Partido Social Democrático 49216
 
8.4%
Movimento Democrático Brasileiro 44949
 
7.6%
Democratas 41941
 
7.1%
Partido dos Trabalhadores 39001
 
6.6%
UNIÃO BRASIL 33538
 
5.7%
Solidariedade 31312
 
5.3%
Partido Liberal 29608
 
5.0%
PROGRESSISTAS 29349
 
5.0%
Partido Democrático Trabalhista 28540
 
4.9%
REPUBLICANOS 19997
 
3.4%
Other values (31) 240509
40.9%

Length

2022-12-06T13:12:23.354499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
partido 319522
22.7%
democrático 122705
 
8.7%
social 111505
 
7.9%
brasileiro 85919
 
6.1%
trabalhista 55616
 
3.9%
liberal 47085
 
3.3%
da 46331
 
3.3%
movimento 44949
 
3.2%
democratas 41941
 
3.0%
brasil 40124
 
2.8%
Other values (39) 494234
35.1%

Most occurring characters

ValueCountFrequency (%)
a 1333803
 
10.8%
i 1227220
 
10.0%
o 1221159
 
9.9%
r 1047629
 
8.5%
821971
 
6.7%
t 680343
 
5.5%
d 653591
 
5.3%
e 640381
 
5.2%
c 508275
 
4.1%
l 483368
 
3.9%
Other values (34) 3684591
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9224890
75.0%
Uppercase Letter 2255470
 
18.3%
Space Separator 821971
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1333803
14.5%
i 1227220
13.3%
o 1221159
13.2%
r 1047629
11.4%
t 680343
7.4%
d 653591
7.1%
e 640381
6.9%
c 508275
 
5.5%
l 483368
 
5.2%
s 427560
 
4.6%
Other values (14) 1001561
10.9%
Uppercase Letter
ValueCountFrequency (%)
P 405367
18.0%
S 353990
15.7%
D 184602
8.2%
B 164307
7.3%
R 156188
 
6.9%
T 137945
 
6.1%
I 120566
 
5.3%
L 117701
 
5.2%
A 114272
 
5.1%
O 93806
 
4.2%
Other values (9) 406726
18.0%
Space Separator
ValueCountFrequency (%)
821971
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11480360
93.3%
Common 821971
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1333803
11.6%
i 1227220
 
10.7%
o 1221159
 
10.6%
r 1047629
 
9.1%
t 680343
 
5.9%
d 653591
 
5.7%
e 640381
 
5.6%
c 508275
 
4.4%
l 483368
 
4.2%
s 427560
 
3.7%
Other values (33) 3257031
28.4%
Common
ValueCountFrequency (%)
821971
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12095151
98.3%
None 207180
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1333803
 
11.0%
i 1227220
 
10.1%
o 1221159
 
10.1%
r 1047629
 
8.7%
821971
 
6.8%
t 680343
 
5.6%
d 653591
 
5.4%
e 640381
 
5.3%
c 508275
 
4.2%
l 483368
 
4.0%
Other values (29) 3477411
28.8%
None
ValueCountFrequency (%)
á 123061
59.4%
ã 36402
 
17.6%
à 33538
 
16.2%
ú 9888
 
4.8%
ç 4291
 
2.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
1
300391 
0
287569 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters587960
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 300391
51.1%
0 287569
48.9%

Length

2022-12-06T13:12:23.456254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:23.572154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 300391
51.1%
0 287569
48.9%

Most occurring characters

ValueCountFrequency (%)
1 300391
51.1%
0 287569
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 587960
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 300391
51.1%
0 287569
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common 587960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 300391
51.1%
0 287569
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 587960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 300391
51.1%
0 287569
48.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.3 MiB
Pessoa Jurídica
300391 
Pessoa Física
287569 

Length

Max length15
Median length15
Mean length14.021808
Min length13

Characters and Unicode

Total characters8244262
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPessoa Jurídica
2nd rowPessoa Jurídica
3rd rowPessoa Jurídica
4th rowPessoa Jurídica
5th rowPessoa Jurídica

Common Values

ValueCountFrequency (%)
Pessoa Jurídica 300391
51.1%
Pessoa Física 287569
48.9%

Length

2022-12-06T13:12:23.672223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:23.788120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
pessoa 587960
50.0%
jurídica 300391
25.5%
física 287569
24.5%

Most occurring characters

ValueCountFrequency (%)
s 1463489
17.8%
a 1175920
14.3%
P 587960
7.1%
e 587960
7.1%
o 587960
7.1%
587960
7.1%
í 587960
7.1%
i 587960
7.1%
c 587960
7.1%
J 300391
 
3.6%
Other values (4) 1188742
14.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6480382
78.6%
Uppercase Letter 1175920
 
14.3%
Space Separator 587960
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1463489
22.6%
a 1175920
18.1%
e 587960
9.1%
o 587960
9.1%
í 587960
9.1%
i 587960
9.1%
c 587960
9.1%
u 300391
 
4.6%
r 300391
 
4.6%
d 300391
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
P 587960
50.0%
J 300391
25.5%
F 287569
24.5%
Space Separator
ValueCountFrequency (%)
587960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7656302
92.9%
Common 587960
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1463489
19.1%
a 1175920
15.4%
P 587960
7.7%
e 587960
7.7%
o 587960
7.7%
í 587960
7.7%
i 587960
7.7%
c 587960
7.7%
J 300391
 
3.9%
u 300391
 
3.9%
Other values (3) 888351
11.6%
Common
ValueCountFrequency (%)
587960
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7656302
92.9%
None 587960
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 1463489
19.1%
a 1175920
15.4%
P 587960
7.7%
e 587960
7.7%
o 587960
7.7%
587960
7.7%
i 587960
7.7%
c 587960
7.7%
J 300391
 
3.9%
u 300391
 
3.9%
Other values (3) 888351
11.6%
None
ValueCountFrequency (%)
í 587960
100.0%

CD_CNAE_FORNECEDOR
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct319
Distinct (%)0.1%
Missing349188
Missing (%)59.4%
Infinite0
Infinite (%)0.0%
Mean43077.757
Minimum1512
Maximum96092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:23.919649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1512
5-th percentile18113
Q118130
median47318
Q358191
95-th percentile82199
Maximum96092
Range94580
Interquartile range (IQR)40061

Descriptive statistics

Standard deviation22230.187
Coefficient of variation (CV)0.51604793
Kurtosis-1.0652793
Mean43077.757
Median Absolute Deviation (MAD)25796
Skewness0.23208988
Sum1.0285762 × 1010
Variance4.9418123 × 108
MonotonicityNot monotonic
2022-12-06T13:12:24.062813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18130 53928
 
9.2%
47318 27915
 
4.7%
58115 22069
 
3.8%
18113 12159
 
2.1%
58298 11111
 
1.9%
18211 8532
 
1.5%
17311 6787
 
1.2%
32990 5869
 
1.0%
47610 5384
 
0.9%
74901 4469
 
0.8%
Other values (309) 80549
 
13.7%
(Missing) 349188
59.4%
ValueCountFrequency (%)
1512 2
 
< 0.1%
2101 1
 
< 0.1%
8924 4
 
< 0.1%
10317 6
 
< 0.1%
10911 105
< 0.1%
10945 1
 
< 0.1%
10961 5
 
< 0.1%
10996 39
 
< 0.1%
11216 15
 
< 0.1%
13308 11
 
< 0.1%
ValueCountFrequency (%)
96092 117
 
< 0.1%
96033 1
 
< 0.1%
96025 27
 
< 0.1%
95291 50
 
< 0.1%
95215 7
 
< 0.1%
95126 31
 
< 0.1%
95118 241
 
< 0.1%
94995 262
 
< 0.1%
94936 17
 
< 0.1%
94928 2142
0.4%

DS_CNAE_FORNECEDOR
Categorical

HIGH CARDINALITY
MISSING

Distinct319
Distinct (%)0.1%
Missing349188
Missing (%)59.4%
Memory size43.5 MiB
Impressão de materiais para outros usos
53928 
Comércio varejista de combustíveis para veículos automotores
27915 
Edição de livros
22069 
Impressão de jornais, livros, revistas e outras publicações periódicas
12159 
Edição integrada à impressão de cadastros, listas e outros produtos gráficos
 
11111
Other values (314)
111590 

Length

Max length144
Median length130
Mean length51.546479
Min length7

Characters and Unicode

Total characters12307856
Distinct characters61
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)< 0.1%

Sample

1st rowEdição de livros
2nd rowEdição de livros
3rd rowEdição de livros
4th rowEdição de livros
5th rowEdição de livros

Common Values

ValueCountFrequency (%)
Impressão de materiais para outros usos 53928
 
9.2%
Comércio varejista de combustíveis para veículos automotores 27915
 
4.7%
Edição de livros 22069
 
3.8%
Impressão de jornais, livros, revistas e outras publicações periódicas 12159
 
2.1%
Edição integrada à impressão de cadastros, listas e outros produtos gráficos 11111
 
1.9%
Serviços de pré-impressão 8532
 
1.5%
Fabricação de embalagens de papel 6787
 
1.2%
Fabricação de produtos diversos não especificados anteriormente 5869
 
1.0%
Comércio varejista de livros, jornais, revistas e papelaria 5384
 
0.9%
Atividades profissionais, científicas e técnicas não especificadas anteriormente 4469
 
0.8%
Other values (309) 80549
 
13.7%
(Missing) 349188
59.4%

Length

2022-12-06T13:12:24.247800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 271348
 
16.5%
para 88144
 
5.4%
e 80987
 
4.9%
impressão 80270
 
4.9%
outros 76637
 
4.7%
materiais 54287
 
3.3%
usos 53929
 
3.3%
comércio 49186
 
3.0%
livros 44079
 
2.7%
varejista 43915
 
2.7%
Other values (587) 797167
48.6%

Most occurring characters

ValueCountFrequency (%)
1401177
11.4%
s 1193375
 
9.7%
e 1153345
 
9.4%
o 1102892
 
9.0%
a 1010424
 
8.2%
i 900715
 
7.3%
r 887973
 
7.2%
t 609716
 
5.0%
d 550157
 
4.5%
p 401858
 
3.3%
Other values (51) 3096224
25.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10562806
85.8%
Space Separator 1401177
 
11.4%
Uppercase Letter 241383
 
2.0%
Other Punctuation 89977
 
0.7%
Dash Punctuation 12501
 
0.1%
Open Punctuation 6
 
< 0.1%
Close Punctuation 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1193375
11.3%
e 1153345
10.9%
o 1102892
10.4%
a 1010424
9.6%
i 900715
 
8.5%
r 887973
 
8.4%
t 609716
 
5.8%
d 550157
 
5.2%
p 401858
 
3.8%
c 399848
 
3.8%
Other values (26) 2352503
22.3%
Uppercase Letter
ValueCountFrequency (%)
I 68020
28.2%
C 52427
21.7%
E 41413
17.2%
A 34294
14.2%
F 18488
 
7.7%
S 13005
 
5.4%
O 3974
 
1.6%
R 2657
 
1.1%
T 2443
 
1.0%
D 1362
 
0.6%
Other values (9) 3300
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 86943
96.6%
; 3034
 
3.4%
Space Separator
ValueCountFrequency (%)
1401177
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12501
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10804189
87.8%
Common 1503667
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1193375
11.0%
e 1153345
10.7%
o 1102892
10.2%
a 1010424
 
9.4%
i 900715
 
8.3%
r 887973
 
8.2%
t 609716
 
5.6%
d 550157
 
5.1%
p 401858
 
3.7%
c 399848
 
3.7%
Other values (45) 2593886
24.0%
Common
ValueCountFrequency (%)
1401177
93.2%
, 86943
 
5.8%
- 12501
 
0.8%
; 3034
 
0.2%
( 6
 
< 0.1%
) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11720743
95.2%
None 587113
 
4.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1401177
12.0%
s 1193375
10.2%
e 1153345
9.8%
o 1102892
9.4%
a 1010424
8.6%
i 900715
 
7.7%
r 887973
 
7.6%
t 609716
 
5.2%
d 550157
 
4.7%
p 401858
 
3.4%
Other values (38) 2509111
21.4%
None
ValueCountFrequency (%)
ã 199357
34.0%
ç 134004
22.8%
í 74757
 
12.7%
é 67317
 
11.5%
á 34799
 
5.9%
õ 24660
 
4.2%
ó 23762
 
4.0%
à 18512
 
3.2%
ê 5183
 
0.9%
ú 2626
 
0.4%
Other values (3) 2136
 
0.4%

NR_CPF_CNPJ_FORNECEDOR
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct238195
Distinct (%)45.2%
Missing61023
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean8.5277171 × 1012
Minimum0
Maximum9.7553165 × 1013
Zeros50
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:25.327957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2486118 × 109
Q11.2667687 × 1010
median5.9832461 × 1010
Q31.457253 × 1013
95-th percentile3.4557937 × 1013
Maximum9.7553165 × 1013
Range9.7553165 × 1013
Interquartile range (IQR)1.4559862 × 1013

Descriptive statistics

Standard deviation1.2663552 × 1013
Coefficient of variation (CV)1.4849874
Kurtosis1.8562477
Mean8.5277171 × 1012
Median Absolute Deviation (MAD)5.6780617 × 1010
Skewness1.5053082
Sum4.4935697 × 1018
Variance1.6036556 × 1026
MonotonicityNot monotonic
2022-12-06T13:12:26.901311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14572530000119.0 19126
 
3.3%
23412180000114.0 6483
 
1.1%
28582112000117.0 3862
 
0.7%
2495060000158.0 3815
 
0.6%
971215000150.0 3778
 
0.6%
13347016000117.0 3467
 
0.6%
14796606000190.0 3087
 
0.5%
25021356000132.0 2730
 
0.5%
509320000171.0 2255
 
0.4%
2147077000114.0 2214
 
0.4%
Other values (238185) 476120
81.0%
(Missing) 61023
 
10.4%
ValueCountFrequency (%)
0.0 50
 
< 0.1%
1.0 9
 
< 0.1%
2.0 2
 
< 0.1%
3.0 2
 
< 0.1%
4.0 2
 
< 0.1%
5.0 1
 
< 0.1%
6.0 1
 
< 0.1%
7.0 1
 
< 0.1%
12.0 1
 
< 0.1%
191.0 313
0.1%
ValueCountFrequency (%)
97553165000122.0 4
< 0.1%
97548962000111.0 7
< 0.1%
97546668000170.0 4
< 0.1%
97545969000180.0 6
< 0.1%
97541649000232.0 1
 
< 0.1%
97540783000138.0 2
 
< 0.1%
97537646000144.0 5
< 0.1%
97534110000175.0 1
 
< 0.1%
97532657000131.0 1
 
< 0.1%
97532367000198.0 1
 
< 0.1%

NM_FORNECEDOR
Categorical

HIGH CARDINALITY
MISSING

Distinct251160
Distinct (%)47.7%
Missing61023
Missing (%)10.4%
Memory size48.0 MiB
APEL GRAFICA E EDITORA LTDA
 
10183
APEL GRÁFICA E EDITORA LTDA.
 
5923
EXACT INDUSTRIA E SERVIÇOS DE EMBALAGENS E ROTULOS LTDA
 
3966
3 GRAPH GRAFICA E EDITORA LTDA
 
3502
LUANA ARTES GRÁFICAS E EDITORA EIRELI - ME
 
2844
Other values (251155)
500519 

Length

Max length150
Median length96
Mean length28.994671
Min length3

Characters and Unicode

Total characters15278365
Distinct characters91
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique201892 ?
Unique (%)38.3%

Sample

1st rowAPEL GRÁFICA E EDITORA LTDA
2nd rowAPEL GRÁFICA E EDITORA LTDA
3rd rowAPEL GRÁFICA E EDITORA LTDA
4th rowAPEL GRÁFICA E EDITORA LTDA
5th rowAPEL GRÁFICA E EDITORA LTDA

Common Values

ValueCountFrequency (%)
APEL GRAFICA E EDITORA LTDA 10183
 
1.7%
APEL GRÁFICA E EDITORA LTDA. 5923
 
1.0%
EXACT INDUSTRIA E SERVIÇOS DE EMBALAGENS E ROTULOS LTDA 3966
 
0.7%
3 GRAPH GRAFICA E EDITORA LTDA 3502
 
0.6%
LUANA ARTES GRÁFICAS E EDITORA EIRELI - ME 2844
 
0.5%
IMPRINT 2001 GRAFICA E EDITORA LTDA 2365
 
0.4%
APEL GRÁFICA E EDITORA LTDA 1636
 
0.3%
NOVA COLOR GRAFICA E EDITORA LTDA 1552
 
0.3%
GRAFICA E EDITORA PRINCIPE DA PAZ EPP 1339
 
0.2%
STATUS CONSTRUÇÕES PUBLICIDADES E EVENTOS LTDA ME 1300
 
0.2%
Other values (251150) 492327
83.7%
(Missing) 61023
 
10.4%

Length

2022-12-06T13:12:27.449526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ltda 137633
 
5.6%
de 125527
 
5.1%
e 107975
 
4.4%
da 81075
 
3.3%
silva 74767
 
3.1%
editora 57235
 
2.3%
grafica 55667
 
2.3%
santos 39842
 
1.6%
me 35260
 
1.4%
dos 32204
 
1.3%
Other values (50097) 1697210
69.4%

Most occurring characters

ValueCountFrequency (%)
1977725
12.9%
A 1947325
12.7%
E 1336530
 
8.7%
I 1254538
 
8.2%
O 1050959
 
6.9%
R 1040577
 
6.8%
S 914270
 
6.0%
L 767501
 
5.0%
D 745671
 
4.9%
T 620731
 
4.1%
Other values (81) 3622538
23.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13109422
85.8%
Space Separator 1977725
 
12.9%
Decimal Number 117858
 
0.8%
Other Punctuation 30885
 
0.2%
Lowercase Letter 21159
 
0.1%
Dash Punctuation 19630
 
0.1%
Math Symbol 961
 
< 0.1%
Connector Punctuation 405
 
< 0.1%
Close Punctuation 156
 
< 0.1%
Open Punctuation 148
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1947325
14.9%
E 1336530
10.2%
I 1254538
9.6%
O 1050959
 
8.0%
R 1040577
 
7.9%
S 914270
 
7.0%
L 767501
 
5.9%
D 745671
 
5.7%
T 620731
 
4.7%
N 577166
 
4.4%
Other values (29) 2854154
21.8%
Lowercase Letter
ValueCountFrequency (%)
i 2924
13.8%
o 2911
13.8%
a 2139
10.1%
e 1945
9.2%
r 1502
 
7.1%
c 1219
 
5.8%
s 1069
 
5.1%
n 1038
 
4.9%
l 961
 
4.5%
t 813
 
3.8%
Other values (15) 4638
21.9%
Decimal Number
ValueCountFrequency (%)
0 23117
19.6%
1 15918
13.5%
2 15229
12.9%
7 13311
11.3%
3 12321
10.5%
4 9099
 
7.7%
8 8315
 
7.1%
9 7065
 
6.0%
5 7063
 
6.0%
6 6420
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 28115
91.0%
/ 1661
 
5.4%
· 1077
 
3.5%
: 22
 
0.1%
? 6
 
< 0.1%
! 4
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 956
99.5%
| 5
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 146
98.6%
[ 2
 
1.4%
Close Punctuation
ValueCountFrequency (%)
) 145
92.9%
] 11
 
7.1%
Modifier Symbol
ValueCountFrequency (%)
` 15
93.8%
´ 1
 
6.2%
Space Separator
ValueCountFrequency (%)
1977725
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19630
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 405
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13130581
85.9%
Common 2147784
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1947325
14.8%
E 1336530
10.2%
I 1254538
9.6%
O 1050959
 
8.0%
R 1040577
 
7.9%
S 914270
 
7.0%
L 767501
 
5.8%
D 745671
 
5.7%
T 620731
 
4.7%
N 577166
 
4.4%
Other values (54) 2875313
21.9%
Common
ValueCountFrequency (%)
1977725
92.1%
. 28115
 
1.3%
0 23117
 
1.1%
- 19630
 
0.9%
1 15918
 
0.7%
2 15229
 
0.7%
7 13311
 
0.6%
3 12321
 
0.6%
4 9099
 
0.4%
8 8315
 
0.4%
Other values (17) 25004
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120194
99.0%
None 158171
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1977725
13.1%
A 1947325
12.9%
E 1336530
 
8.8%
I 1254538
 
8.3%
O 1050959
 
7.0%
R 1040577
 
6.9%
S 914270
 
6.0%
L 767501
 
5.1%
D 745671
 
4.9%
T 620731
 
4.1%
Other values (63) 3464367
22.9%
None
ValueCountFrequency (%)
Ç 66980
42.3%
à 38559
24.4%
Á 26532
 
16.8%
Õ 7559
 
4.8%
É 6293
 
4.0%
Í 3545
 
2.2%
Ó 2715
 
1.7%
Ú 1625
 
1.0%
· 1077
 
0.7%
Ô 791
 
0.5%
Other values (8) 2495
 
1.6%

NM_FORNECEDOR_RFB
Categorical

HIGH CARDINALITY
MISSING

Distinct231292
Distinct (%)43.9%
Missing61598
Missing (%)10.5%
Memory size46.0 MiB
APEL GRAFICA E EDITORA LTDA
 
19127
EXACT INDUSTRIA E SERVICOS DE EMBALAGENS E ROTULOS LTDA
 
6483
LUANA ARTES GRAFICAS E EDITORA EIRELI
 
3862
IMPRINT 2001 GRAFICA E EDITORA LTDA
 
3815
3 GRAPH GRAFICA E EDITORA EIRELI
 
3778
Other values (231287)
489297 

Length

Max length150
Median length95
Mean length29.940522
Min length7

Characters and Unicode

Total characters15759553
Distinct characters68
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182407 ?
Unique (%)34.7%

Sample

1st rowAPEL GRAFICA E EDITORA LTDA
2nd rowAPEL GRAFICA E EDITORA LTDA
3rd rowAPEL GRAFICA E EDITORA LTDA
4th rowAPEL GRAFICA E EDITORA LTDA
5th rowAPEL GRAFICA E EDITORA LTDA

Common Values

ValueCountFrequency (%)
APEL GRAFICA E EDITORA LTDA 19127
 
3.3%
EXACT INDUSTRIA E SERVICOS DE EMBALAGENS E ROTULOS LTDA 6483
 
1.1%
LUANA ARTES GRAFICAS E EDITORA EIRELI 3862
 
0.7%
IMPRINT 2001 GRAFICA E EDITORA LTDA 3815
 
0.6%
3 GRAPH GRAFICA E EDITORA EIRELI 3778
 
0.6%
FACEBOOK SERVICOS ONLINE DO BRASIL LTDA. 3467
 
0.6%
ADYEN DO BRASIL INSTITUICAO DE PAGAMENTO LTDA. 3086
 
0.5%
DLOCAL BRASIL INSTITUICAO DE PAGAMENTO S.A. 2723
 
0.5%
PIRAMIDE DIGITAL IMPRESSOES EIRELI 2255
 
0.4%
EDG EDITORA GRAFICA EIRELI 2214
 
0.4%
Other values (231282) 475552
80.9%
(Missing) 61598
 
10.5%

Length

2022-12-06T13:12:38.753174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ltda 148509
 
6.0%
de 137529
 
5.5%
e 113719
 
4.6%
da 83105
 
3.3%
silva 76224
 
3.1%
grafica 70826
 
2.9%
editora 59268
 
2.4%
eireli 45223
 
1.8%
santos 40566
 
1.6%
dos 32943
 
1.3%
Other values (43561) 1673366
67.4%

Most occurring characters

ValueCountFrequency (%)
A 2074186
13.2%
1956721
12.4%
E 1363339
 
8.7%
I 1335365
 
8.5%
O 1106069
 
7.0%
R 1079647
 
6.9%
S 955097
 
6.1%
L 800525
 
5.1%
D 778854
 
4.9%
T 656107
 
4.2%
Other values (58) 3653643
23.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13523672
85.8%
Space Separator 1956721
 
12.4%
Decimal Number 222762
 
1.4%
Other Punctuation 43805
 
0.3%
Lowercase Letter 6585
 
< 0.1%
Dash Punctuation 4851
 
< 0.1%
Math Symbol 1127
 
< 0.1%
Open Punctuation 15
 
< 0.1%
Close Punctuation 15
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2074186
15.3%
E 1363339
10.1%
I 1335365
9.9%
O 1106069
 
8.2%
R 1079647
 
8.0%
S 955097
 
7.1%
L 800525
 
5.9%
D 778854
 
5.8%
T 656107
 
4.9%
N 597933
 
4.4%
Other values (16) 2776550
20.5%
Lowercase Letter
ValueCountFrequency (%)
a 1371
20.8%
e 828
12.6%
o 627
9.5%
c 618
9.4%
r 586
8.9%
d 558
8.5%
u 360
 
5.5%
s 326
 
5.0%
m 209
 
3.2%
t 209
 
3.2%
Other values (9) 893
13.6%
Decimal Number
ValueCountFrequency (%)
0 38255
17.2%
1 29847
13.4%
7 29132
13.1%
2 25554
11.5%
3 20974
9.4%
4 17554
7.9%
8 16897
7.6%
5 14892
 
6.7%
9 14834
 
6.7%
6 14823
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 33275
76.0%
, 4433
 
10.1%
& 3795
 
8.7%
/ 1972
 
4.5%
' 318
 
0.7%
? 7
 
< 0.1%
! 4
 
< 0.1%
: 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1956721
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4851
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1127
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13530257
85.9%
Common 2229296
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2074186
15.3%
E 1363339
10.1%
I 1335365
9.9%
O 1106069
 
8.2%
R 1079647
 
8.0%
S 955097
 
7.1%
L 800525
 
5.9%
D 778854
 
5.8%
T 656107
 
4.8%
N 597933
 
4.4%
Other values (35) 2783135
20.6%
Common
ValueCountFrequency (%)
1956721
87.8%
0 38255
 
1.7%
. 33275
 
1.5%
1 29847
 
1.3%
7 29132
 
1.3%
2 25554
 
1.1%
3 20974
 
0.9%
4 17554
 
0.8%
8 16897
 
0.8%
5 14892
 
0.7%
Other values (13) 46195
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15759553
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2074186
13.2%
1956721
12.4%
E 1363339
 
8.7%
I 1335365
 
8.5%
O 1106069
 
7.0%
R 1079647
 
6.9%
S 955097
 
6.1%
L 800525
 
5.1%
D 778854
 
4.9%
T 656107
 
4.2%
Other values (58) 3653643
23.2%

CD_ESFERA_PART_FORNECEDOR
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing587960
Missing (%)100.0%
Memory size5.0 MiB

DS_ESFERA_PART_FORNECEDOR
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing587960
Missing (%)100.0%
Memory size5.0 MiB

SG_UF_FORNECEDOR
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.4%
Missing585869
Missing (%)99.6%
Memory size22.5 MiB
RJ
2044 
BR
 
28
MG
 
7
TO
 
6
SP
 
3
Other values (3)
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4182
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowRJ
2nd rowRJ
3rd rowRJ
4th rowRJ
5th rowRJ

Common Values

ValueCountFrequency (%)
RJ 2044
 
0.3%
BR 28
 
< 0.1%
MG 7
 
< 0.1%
TO 6
 
< 0.1%
SP 3
 
< 0.1%
RN 1
 
< 0.1%
DF 1
 
< 0.1%
PB 1
 
< 0.1%
(Missing) 585869
99.6%

Length

2022-12-06T13:12:38.873981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:38.988428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
rj 2044
97.8%
br 28
 
1.3%
mg 7
 
0.3%
to 6
 
0.3%
sp 3
 
0.1%
rn 1
 
< 0.1%
df 1
 
< 0.1%
pb 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
R 2073
49.6%
J 2044
48.9%
B 29
 
0.7%
M 7
 
0.2%
G 7
 
0.2%
T 6
 
0.1%
O 6
 
0.1%
P 4
 
0.1%
S 3
 
0.1%
N 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4182
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 2073
49.6%
J 2044
48.9%
B 29
 
0.7%
M 7
 
0.2%
G 7
 
0.2%
T 6
 
0.1%
O 6
 
0.1%
P 4
 
0.1%
S 3
 
0.1%
N 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4182
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 2073
49.6%
J 2044
48.9%
B 29
 
0.7%
M 7
 
0.2%
G 7
 
0.2%
T 6
 
0.1%
O 6
 
0.1%
P 4
 
0.1%
S 3
 
0.1%
N 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 2073
49.6%
J 2044
48.9%
B 29
 
0.7%
M 7
 
0.2%
G 7
 
0.2%
T 6
 
0.1%
O 6
 
0.1%
P 4
 
0.1%
S 3
 
0.1%
N 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

CD_MUNICIPIO_FORNECEDOR
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct62
Distinct (%)6.0%
Missing586933
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean58735.448
Minimum16195
Maximum97012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:39.120285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16195
5-th percentile58092
Q158327
median58556
Q359110
95-th percentile60011
Maximum97012
Range80817
Interquartile range (IQR)783

Descriptive statistics

Standard deviation1873.0287
Coefficient of variation (CV)0.031889239
Kurtosis428.86645
Mean58735.448
Median Absolute Deviation (MAD)353
Skewness-3.0802083
Sum60321305
Variance3508236.6
MonotonicityNot monotonic
2022-12-06T13:12:39.263016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60011 107
 
< 0.1%
58203 79
 
< 0.1%
58475 54
 
< 0.1%
59153 54
 
< 0.1%
58777 52
 
< 0.1%
58335 50
 
< 0.1%
58394 44
 
< 0.1%
58157 38
 
< 0.1%
58327 38
 
< 0.1%
58971 36
 
< 0.1%
Other values (52) 475
 
0.1%
(Missing) 586933
99.8%
ValueCountFrequency (%)
16195 1
 
< 0.1%
58009 7
 
< 0.1%
58017 10
 
< 0.1%
58033 1
 
< 0.1%
58041 4
 
< 0.1%
58068 1
 
< 0.1%
58076 26
< 0.1%
58092 4
 
< 0.1%
58106 6
 
< 0.1%
58122 4
 
< 0.1%
ValueCountFrequency (%)
97012 1
 
< 0.1%
60011 107
< 0.1%
59331 3
 
< 0.1%
59293 1
 
< 0.1%
59250 24
 
< 0.1%
59234 1
 
< 0.1%
59218 26
 
< 0.1%
59196 29
 
< 0.1%
59153 54
< 0.1%
59137 5
 
< 0.1%

NM_MUNICIPIO_FORNECEDOR
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct62
Distinct (%)6.0%
Missing586933
Missing (%)99.8%
Memory size22.5 MiB
RIO DE JANEIRO
107 
RIO DAS OSTRAS
79 
TERESÓPOLIS
 
54
MACAÉ
 
54
PETRÓPOLIS
 
52
Other values (57)
681 

Length

Max length29
Median length21
Mean length11.256086
Min length4

Characters and Unicode

Total characters11560
Distinct characters32
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)1.2%

Sample

1st rowBARRA MANSA
2nd rowPETRÓPOLIS
3rd rowPETRÓPOLIS
4th rowCANTAGALO
5th rowRESENDE

Common Values

ValueCountFrequency (%)
RIO DE JANEIRO 107
 
< 0.1%
RIO DAS OSTRAS 79
 
< 0.1%
TERESÓPOLIS 54
 
< 0.1%
MACAÉ 54
 
< 0.1%
PETRÓPOLIS 52
 
< 0.1%
DUQUE DE CAXIAS 50
 
< 0.1%
ITAGUAÍ 44
 
< 0.1%
PORTO REAL 38
 
< 0.1%
CACHOEIRAS DE MACACU 38
 
< 0.1%
SÃO GONÇALO 36
 
< 0.1%
Other values (52) 475
 
0.1%
(Missing) 586933
99.8%

Length

2022-12-06T13:12:39.385713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 231
 
11.5%
rio 191
 
9.5%
janeiro 107
 
5.3%
ostras 79
 
3.9%
das 79
 
3.9%
são 79
 
3.9%
teresópolis 54
 
2.7%
macaé 54
 
2.7%
petrópolis 52
 
2.6%
duque 50
 
2.5%
Other values (81) 1031
51.4%

Most occurring characters

ValueCountFrequency (%)
A 1551
13.4%
O 1101
 
9.5%
R 1010
 
8.7%
980
 
8.5%
I 970
 
8.4%
E 914
 
7.9%
S 812
 
7.0%
D 520
 
4.5%
T 457
 
4.0%
C 367
 
3.2%
Other values (22) 2878
24.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10580
91.5%
Space Separator 980
 
8.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1551
14.7%
O 1101
10.4%
R 1010
9.5%
I 970
 
9.2%
E 914
 
8.6%
S 812
 
7.7%
D 520
 
4.9%
T 457
 
4.3%
C 367
 
3.5%
N 363
 
3.4%
Other values (21) 2515
23.8%
Space Separator
ValueCountFrequency (%)
980
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10580
91.5%
Common 980
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1551
14.7%
O 1101
10.4%
R 1010
9.5%
I 970
 
9.2%
E 914
 
8.6%
S 812
 
7.7%
D 520
 
4.9%
T 457
 
4.3%
C 367
 
3.5%
N 363
 
3.4%
Other values (21) 2515
23.8%
Common
ValueCountFrequency (%)
980
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11039
95.5%
None 521
 
4.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1551
14.1%
O 1101
10.0%
R 1010
9.1%
980
8.9%
I 970
8.8%
E 914
 
8.3%
S 812
 
7.4%
D 520
 
4.7%
T 457
 
4.1%
C 367
 
3.3%
Other values (15) 2357
21.4%
None
ValueCountFrequency (%)
Ó 129
24.8%
à 124
23.8%
Ç 87
16.7%
É 79
15.2%
Í 55
10.6%
Ê 29
 
5.6%
Á 18
 
3.5%

SQ_CANDIDATO_FORNECEDOR
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1148
Distinct (%)65.9%
Missing586217
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean1.9043122 × 1011
Minimum1.900006 × 1011
Maximum2.8000061 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:39.495290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.900006 × 1011
5-th percentile1.900006 × 1011
Q11.9000061 × 1011
median1.9000078 × 1011
Q31.9000111 × 1011
95-th percentile1.9000164 × 1011
Maximum2.8000061 × 1011
Range9.0000007 × 1010
Interquartile range (IQR)498930.5

Descriptive statistics

Standard deviation5.719114 × 109
Coefficient of variation (CV)0.030032439
Kurtosis181.4963
Mean1.9043122 × 1011
Median Absolute Deviation (MAD)170710
Skewness13.442481
Sum3.3192162 × 1014
Variance3.2708265 × 1019
MonotonicityNot monotonic
2022-12-06T13:12:39.623059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190000628286.0 13
 
< 0.1%
190000613609.0 11
 
< 0.1%
190001234426.0 11
 
< 0.1%
190001609545.0 9
 
< 0.1%
190001613662.0 8
 
< 0.1%
190000611488.0 7
 
< 0.1%
190000610708.0 7
 
< 0.1%
190000611435.0 7
 
< 0.1%
190001654228.0 6
 
< 0.1%
190000601112.0 6
 
< 0.1%
Other values (1138) 1658
 
0.3%
(Missing) 586217
99.7%
ValueCountFrequency (%)
190000601075.0 1
 
< 0.1%
190000601078.0 4
< 0.1%
190000601079.0 2
< 0.1%
190000601083.0 4
< 0.1%
190000601084.0 2
< 0.1%
190000601087.0 1
 
< 0.1%
190000601089.0 2
< 0.1%
190000601094.0 1
 
< 0.1%
190000601098.0 4
< 0.1%
190000601100.0 1
 
< 0.1%
ValueCountFrequency (%)
280000607640.0 1
< 0.1%
270001649251.0 1
< 0.1%
270001649250.0 1
< 0.1%
270001649249.0 1
< 0.1%
270001643223.0 1
< 0.1%
270001643222.0 1
< 0.1%
270001643221.0 1
< 0.1%
250000605732.0 1
< 0.1%
250000605458.0 2
< 0.1%
190001731347.0 2
< 0.1%

NR_CANDIDATO_FORNECEDOR
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct932
Distinct (%)52.7%
Missing586190
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean33897.82
Minimum10
Maximum90999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:39.753546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile3036
Q114001
median23458
Q350555
95-th percentile90222
Maximum90999
Range90989
Interquartile range (IQR)36554

Descriptive statistics

Standard deviation25272.053
Coefficient of variation (CV)0.74553625
Kurtosis-0.21463544
Mean33897.82
Median Absolute Deviation (MAD)13224
Skewness0.85889025
Sum59999141
Variance6.3867668 × 108
MonotonicityNot monotonic
2022-12-06T13:12:39.876777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 16
 
< 0.1%
5101 13
 
< 0.1%
30330 13
 
< 0.1%
17000 11
 
< 0.1%
50050 11
 
< 0.1%
6565 11
 
< 0.1%
65 11
 
< 0.1%
90123 10
 
< 0.1%
50800 10
 
< 0.1%
50000 9
 
< 0.1%
Other values (922) 1655
 
0.3%
(Missing) 586190
99.7%
ValueCountFrequency (%)
10 4
 
< 0.1%
11 1
 
< 0.1%
13 2
 
< 0.1%
17 16
< 0.1%
21 2
 
< 0.1%
23 1
 
< 0.1%
25 1
 
< 0.1%
30 5
 
< 0.1%
43 1
 
< 0.1%
55 1
 
< 0.1%
ValueCountFrequency (%)
90999 3
< 0.1%
90991 1
 
< 0.1%
90949 2
< 0.1%
90922 2
< 0.1%
90909 1
 
< 0.1%
90888 1
 
< 0.1%
90864 1
 
< 0.1%
90840 2
< 0.1%
90812 1
 
< 0.1%
90800 4
< 0.1%

CD_CARGO_FORNECEDOR
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)0.4%
Missing586190
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean9.5960452
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:39.954914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q313
95-th percentile13
Maximum13
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.044625
Coefficient of variation (CV)0.31727914
Kurtosis-1.864533
Mean9.5960452
Median Absolute Deviation (MAD)1
Skewness0.16174164
Sum16985
Variance9.2697413
MonotonicityNot monotonic
2022-12-06T13:12:40.055232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 833
 
0.1%
13 758
 
0.1%
6 129
 
< 0.1%
11 36
 
< 0.1%
12 10
 
< 0.1%
3 3
 
< 0.1%
1 1
 
< 0.1%
(Missing) 586190
99.7%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 3
 
< 0.1%
6 129
 
< 0.1%
7 833
0.1%
11 36
 
< 0.1%
12 10
 
< 0.1%
13 758
0.1%
ValueCountFrequency (%)
13 758
0.1%
12 10
 
< 0.1%
11 36
 
< 0.1%
7 833
0.1%
6 129
 
< 0.1%
3 3
 
< 0.1%
1 1
 
< 0.1%

DS_CARGO_FORNECEDOR
Categorical

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)0.4%
Missing586190
Missing (%)99.7%
Memory size22.5 MiB
Deputado Estadual
833 
Vereador
758 
Deputado Federal
129 
Prefeito
 
36
Vice-prefeito
 
10
Other values (2)
 
4

Length

Max length17
Median length16
Mean length12.851412
Min length8

Characters and Unicode

Total characters22747
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowDeputado Estadual
2nd rowDeputado Estadual
3rd rowDeputado Estadual
4th rowDeputado Estadual
5th rowDeputado Estadual

Common Values

ValueCountFrequency (%)
Deputado Estadual 833
 
0.1%
Vereador 758
 
0.1%
Deputado Federal 129
 
< 0.1%
Prefeito 36
 
< 0.1%
Vice-prefeito 10
 
< 0.1%
Governador 3
 
< 0.1%
Presidente 1
 
< 0.1%
(Missing) 586190
99.7%

Length

2022-12-06T13:12:40.139739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:40.258315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
deputado 962
35.2%
estadual 833
30.5%
vereador 758
27.7%
federal 129
 
4.7%
prefeito 36
 
1.3%
vice-prefeito 10
 
0.4%
governador 3
 
0.1%
presidente 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 3518
15.5%
e 2844
12.5%
d 2686
11.8%
t 1842
8.1%
u 1795
7.9%
o 1772
7.8%
r 1698
7.5%
p 972
 
4.3%
D 962
 
4.2%
l 962
 
4.2%
Other values (13) 3696
16.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19043
83.7%
Uppercase Letter 2732
 
12.0%
Space Separator 962
 
4.2%
Dash Punctuation 10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3518
18.5%
e 2844
14.9%
d 2686
14.1%
t 1842
9.7%
u 1795
9.4%
o 1772
9.3%
r 1698
8.9%
p 972
 
5.1%
l 962
 
5.1%
s 834
 
4.4%
Other values (5) 120
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
D 962
35.2%
E 833
30.5%
V 768
28.1%
F 129
 
4.7%
P 37
 
1.4%
G 3
 
0.1%
Space Separator
ValueCountFrequency (%)
962
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21775
95.7%
Common 972
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3518
16.2%
e 2844
13.1%
d 2686
12.3%
t 1842
8.5%
u 1795
8.2%
o 1772
8.1%
r 1698
7.8%
p 972
 
4.5%
D 962
 
4.4%
l 962
 
4.4%
Other values (11) 2724
12.5%
Common
ValueCountFrequency (%)
962
99.0%
- 10
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22747
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3518
15.5%
e 2844
12.5%
d 2686
11.8%
t 1842
8.1%
u 1795
7.9%
o 1772
7.8%
r 1698
7.5%
p 972
 
4.3%
D 962
 
4.2%
l 962
 
4.2%
Other values (13) 3696
16.2%

NR_PARTIDO_FORNECEDOR
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct34
Distinct (%)1.6%
Missing585876
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean37.21833
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:40.355737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q117
median28
Q351
95-th percentile90
Maximum90
Range80
Interquartile range (IQR)34

Descriptive statistics

Standard deviation23.89495
Coefficient of variation (CV)0.64202101
Kurtosis-0.38406692
Mean37.21833
Median Absolute Deviation (MAD)15
Skewness0.819415
Sum77563
Variance570.96863
MonotonicityNot monotonic
2022-12-06T13:12:40.471901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
17 192
 
< 0.1%
22 163
 
< 0.1%
90 163
 
< 0.1%
50 158
 
< 0.1%
55 137
 
< 0.1%
11 103
 
< 0.1%
25 98
 
< 0.1%
13 94
 
< 0.1%
51 74
 
< 0.1%
65 71
 
< 0.1%
Other values (24) 831
 
0.1%
(Missing) 585876
99.6%
ValueCountFrequency (%)
10 48
 
< 0.1%
11 103
< 0.1%
12 70
 
< 0.1%
13 94
< 0.1%
14 55
 
< 0.1%
15 34
 
< 0.1%
16 6
 
< 0.1%
17 192
< 0.1%
18 28
 
< 0.1%
19 19
 
< 0.1%
ValueCountFrequency (%)
90 163
< 0.1%
80 22
 
< 0.1%
77 39
 
< 0.1%
70 50
 
< 0.1%
65 71
< 0.1%
55 137
< 0.1%
51 74
< 0.1%
50 158
< 0.1%
45 62
 
< 0.1%
44 32
 
< 0.1%

SG_PARTIDO_FORNECEDOR
Categorical

HIGH CORRELATION
MISSING

Distinct39
Distinct (%)1.9%
Missing585876
Missing (%)99.6%
Memory size22.5 MiB
PSL
192 
PROS
163 
PSOL
158 
PSD
137 
PL
 
125
Other values (34)
1309 

Length

Max length13
Median length12
Mean length3.9558541
Min length2

Characters and Unicode

Total characters8244
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEM
2nd rowDEM
3rd rowMDB
4th rowPROS
5th rowDEM

Common Values

ValueCountFrequency (%)
PSL 192
 
< 0.1%
PROS 163
 
< 0.1%
PSOL 158
 
< 0.1%
PSD 137
 
< 0.1%
PL 125
 
< 0.1%
PP 103
 
< 0.1%
DEM 98
 
< 0.1%
PT 94
 
< 0.1%
PATRIOTA 74
 
< 0.1%
PC do B 71
 
< 0.1%
Other values (29) 869
 
0.1%
(Missing) 585876
99.6%

Length

2022-12-06T13:12:40.616572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
psl 192
 
8.6%
pros 163
 
7.3%
psol 158
 
7.1%
psd 137
 
6.2%
pl 125
 
5.6%
pp 103
 
4.6%
dem 98
 
4.4%
pt 94
 
4.2%
patriota 74
 
3.3%
b 71
 
3.2%
Other values (31) 1011
45.4%

Most occurring characters

ValueCountFrequency (%)
P 1788
21.7%
S 913
11.1%
D 700
 
8.5%
O 624
 
7.6%
L 550
 
6.7%
A 545
 
6.6%
T 445
 
5.4%
R 421
 
5.1%
B 368
 
4.5%
E 337
 
4.1%
Other values (12) 1553
18.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7960
96.6%
Space Separator 142
 
1.7%
Lowercase Letter 142
 
1.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 1788
22.5%
S 913
11.5%
D 700
 
8.8%
O 624
 
7.8%
L 550
 
6.9%
A 545
 
6.8%
T 445
 
5.6%
R 421
 
5.3%
B 368
 
4.6%
E 337
 
4.2%
Other values (9) 1269
15.9%
Lowercase Letter
ValueCountFrequency (%)
d 71
50.0%
o 71
50.0%
Space Separator
ValueCountFrequency (%)
142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8102
98.3%
Common 142
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 1788
22.1%
S 913
11.3%
D 700
 
8.6%
O 624
 
7.7%
L 550
 
6.8%
A 545
 
6.7%
T 445
 
5.5%
R 421
 
5.2%
B 368
 
4.5%
E 337
 
4.2%
Other values (11) 1411
17.4%
Common
ValueCountFrequency (%)
142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8223
99.7%
None 21
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 1788
21.7%
S 913
11.1%
D 700
 
8.5%
O 624
 
7.6%
L 550
 
6.7%
A 545
 
6.6%
T 445
 
5.4%
R 421
 
5.1%
B 368
 
4.5%
E 337
 
4.1%
Other values (11) 1532
18.6%
None
ValueCountFrequency (%)
à 21
100.0%

NM_PARTIDO_FORNECEDOR
Categorical

HIGH CORRELATION
MISSING

Distinct40
Distinct (%)1.9%
Missing585876
Missing (%)99.6%
Memory size22.5 MiB
Partido Social Liberal
192 
Partido Republicano da Ordem Social
163 
Partido Socialismo e Liberdade
158 
Partido Social Democrático
137 
Partido Liberal
 
125
Other values (35)
1309 

Length

Max length46
Median length32
Mean length22.263916
Min length4

Characters and Unicode

Total characters46398
Distinct characters44
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDemocratas
2nd rowDemocratas
3rd rowMovimento Democrático Brasileiro
4th rowPartido Republicano da Ordem Social
5th rowDemocratas

Common Values

ValueCountFrequency (%)
Partido Social Liberal 192
 
< 0.1%
Partido Republicano da Ordem Social 163
 
< 0.1%
Partido Socialismo e Liberdade 158
 
< 0.1%
Partido Social Democrático 137
 
< 0.1%
Partido Liberal 125
 
< 0.1%
Democratas 98
 
< 0.1%
Partido dos Trabalhadores 94
 
< 0.1%
PROGRESSISTAS 76
 
< 0.1%
Patriota 74
 
< 0.1%
Partido Comunista do Brasil 71
 
< 0.1%
Other values (30) 896
 
0.2%
(Missing) 585876
99.6%

Length

2022-12-06T13:12:40.710172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
partido 1474
25.7%
social 593
 
10.3%
da 320
 
5.6%
liberal 317
 
5.5%
democrático 241
 
4.2%
republicano 186
 
3.2%
brasileiro 186
 
3.2%
ordem 163
 
2.8%
socialismo 158
 
2.8%
e 158
 
2.8%
Other values (37) 1943
33.9%

Most occurring characters

ValueCountFrequency (%)
a 5681
12.2%
i 5007
10.8%
o 4429
 
9.5%
r 3816
 
8.2%
3655
 
7.9%
d 2996
 
6.5%
t 2468
 
5.3%
e 2360
 
5.1%
l 2133
 
4.6%
c 1836
 
4.0%
Other values (34) 12017
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35879
77.3%
Uppercase Letter 6864
 
14.8%
Space Separator 3655
 
7.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5681
15.8%
i 5007
14.0%
o 4429
12.3%
r 3816
10.6%
d 2996
8.4%
t 2468
6.9%
e 2360
6.6%
l 2133
 
5.9%
c 1836
 
5.1%
s 1355
 
3.8%
Other values (14) 3798
10.6%
Uppercase Letter
ValueCountFrequency (%)
P 1742
25.4%
S 1266
18.4%
L 532
 
7.8%
R 481
 
7.0%
D 420
 
6.1%
B 389
 
5.7%
T 373
 
5.4%
O 296
 
4.3%
A 242
 
3.5%
C 239
 
3.5%
Other values (9) 884
12.9%
Space Separator
ValueCountFrequency (%)
3655
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42743
92.1%
Common 3655
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5681
13.3%
i 5007
11.7%
o 4429
10.4%
r 3816
 
8.9%
d 2996
 
7.0%
t 2468
 
5.8%
e 2360
 
5.5%
l 2133
 
5.0%
c 1836
 
4.3%
P 1742
 
4.1%
Other values (33) 10275
24.0%
Common
ValueCountFrequency (%)
3655
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45959
99.1%
None 439
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5681
12.4%
i 5007
10.9%
o 4429
 
9.6%
r 3816
 
8.3%
3655
 
8.0%
d 2996
 
6.5%
t 2468
 
5.4%
e 2360
 
5.1%
l 2133
 
4.6%
c 1836
 
4.0%
Other values (29) 11578
25.2%
None
ValueCountFrequency (%)
á 241
54.9%
ã 104
23.7%
ú 38
 
8.7%
ç 35
 
8.0%
à 21
 
4.8%

DS_TIPO_DOCUMENTO
Categorical

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)< 0.1%
Missing62938
Missing (%)10.7%
Memory size36.0 MiB
Outro
221713 
Nota Fiscal
212382 
Recibo
57259 
RPA - Recibo de Pagamento Autônomo
23986 
Fatura
 
5018
Other values (2)
 
4664

Length

Max length34
Median length12
Mean length8.9255974
Min length5

Characters and Unicode

Total characters4686135
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNota Fiscal
2nd rowNota Fiscal
3rd rowNota Fiscal
4th rowNota Fiscal
5th rowNota Fiscal

Common Values

ValueCountFrequency (%)
Outro 221713
37.7%
Nota Fiscal 212382
36.1%
Recibo 57259
 
9.7%
RPA - Recibo de Pagamento Autônomo 23986
 
4.1%
Fatura 5018
 
0.9%
Cupom Fiscal 3402
 
0.6%
Duplicata 1262
 
0.2%
(Missing) 62938
 
10.7%

Length

2022-12-06T13:12:40.827328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T13:12:40.926100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
outro 221713
25.8%
fiscal 215784
25.1%
nota 212382
24.7%
recibo 81245
 
9.4%
rpa 23986
 
2.8%
23986
 
2.8%
de 23986
 
2.8%
pagamento 23986
 
2.8%
autônomo 23986
 
2.8%
fatura 5018
 
0.6%
Other values (2) 4664
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 590700
12.6%
a 488698
 
10.4%
t 488347
 
10.4%
335714
 
7.2%
i 298291
 
6.4%
c 298291
 
6.4%
u 255381
 
5.4%
r 226731
 
4.8%
O 221713
 
4.7%
F 220802
 
4.7%
Other values (17) 1261467
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3465699
74.0%
Uppercase Letter 860736
 
18.4%
Space Separator 335714
 
7.2%
Dash Punctuation 23986
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 590700
17.0%
a 488698
14.1%
t 488347
14.1%
i 298291
8.6%
c 298291
8.6%
u 255381
7.4%
r 226731
 
6.5%
l 217046
 
6.3%
s 215784
 
6.2%
e 129217
 
3.7%
Other values (7) 257213
7.4%
Uppercase Letter
ValueCountFrequency (%)
O 221713
25.8%
F 220802
25.7%
N 212382
24.7%
R 105231
12.2%
P 47972
 
5.6%
A 47972
 
5.6%
C 3402
 
0.4%
D 1262
 
0.1%
Space Separator
ValueCountFrequency (%)
335714
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23986
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4326435
92.3%
Common 359700
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 590700
13.7%
a 488698
11.3%
t 488347
11.3%
i 298291
 
6.9%
c 298291
 
6.9%
u 255381
 
5.9%
r 226731
 
5.2%
O 221713
 
5.1%
F 220802
 
5.1%
l 217046
 
5.0%
Other values (15) 1020435
23.6%
Common
ValueCountFrequency (%)
335714
93.3%
- 23986
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4662149
99.5%
None 23986
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 590700
12.7%
a 488698
 
10.5%
t 488347
 
10.5%
335714
 
7.2%
i 298291
 
6.4%
c 298291
 
6.4%
u 255381
 
5.5%
r 226731
 
4.9%
O 221713
 
4.8%
F 220802
 
4.7%
Other values (16) 1237481
26.5%
None
ValueCountFrequency (%)
ô 23986
100.0%

NR_DOCUMENTO
Categorical

HIGH CARDINALITY
MISSING

Distinct96749
Distinct (%)18.4%
Missing63079
Missing (%)10.7%
Memory size33.3 MiB
SN
107040 
01
 
19710
1
 
18813
001
 
13203
CONTRATO
 
6094
Other values (96744)
360021 

Length

Max length32
Median length28
Mean length4.6674046
Min length1

Characters and Unicode

Total characters2449832
Distinct characters54
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67778 ?
Unique (%)12.9%

Sample

1st row155496 - 1
2nd row155496 - 1
3rd row155496 - 1
4th row155496 - 1
5th row155496 - 1

Common Values

ValueCountFrequency (%)
SN 107040
 
18.2%
01 19710
 
3.4%
1 18813
 
3.2%
001 13203
 
2.2%
CONTRATO 6094
 
1.0%
0001 2653
 
0.5%
SN01 1964
 
0.3%
000001 1963
 
0.3%
002 1920
 
0.3%
02 1692
 
0.3%
Other values (96739) 349829
59.5%
(Missing) 63079
 
10.7%

Length

2022-12-06T13:12:41.095392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sn 108882
 
17.2%
54030
 
8.5%
1 43751
 
6.9%
01 20039
 
3.2%
001 15200
 
2.4%
0 9372
 
1.5%
contrato 6103
 
1.0%
e 4522
 
0.7%
002 3028
 
0.5%
2 2995
 
0.5%
Other values (86085) 365023
57.7%

Most occurring characters

ValueCountFrequency (%)
0 603521
24.6%
1 303501
12.4%
2 197610
 
8.1%
3 137382
 
5.6%
5 128600
 
5.2%
4 125686
 
5.1%
N 124882
 
5.1%
S 118677
 
4.8%
6 115089
 
4.7%
108568
 
4.4%
Other values (44) 486316
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1926284
78.6%
Uppercase Letter 359479
 
14.7%
Space Separator 108568
 
4.4%
Dash Punctuation 54030
 
2.2%
Lowercase Letter 1286
 
0.1%
Other Punctuation 185
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 124882
34.7%
S 118677
33.0%
O 19347
 
5.4%
T 18848
 
5.2%
C 13882
 
3.9%
A 12707
 
3.5%
R 11676
 
3.2%
E 10535
 
2.9%
P 4765
 
1.3%
F 4305
 
1.2%
Other values (16) 19855
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
n 514
40.0%
s 494
38.4%
o 57
 
4.4%
e 51
 
4.0%
m 47
 
3.7%
r 41
 
3.2%
t 33
 
2.6%
u 25
 
1.9%
a 18
 
1.4%
c 2
 
0.2%
Other values (4) 4
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 603521
31.3%
1 303501
15.8%
2 197610
 
10.3%
3 137382
 
7.1%
5 128600
 
6.7%
4 125686
 
6.5%
6 115089
 
6.0%
8 108126
 
5.6%
7 104831
 
5.4%
9 101938
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 183
98.9%
/ 2
 
1.1%
Space Separator
ValueCountFrequency (%)
108568
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54030
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2089067
85.3%
Latin 360765
 
14.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 124882
34.6%
S 118677
32.9%
O 19347
 
5.4%
T 18848
 
5.2%
C 13882
 
3.8%
A 12707
 
3.5%
R 11676
 
3.2%
E 10535
 
2.9%
P 4765
 
1.3%
F 4305
 
1.2%
Other values (30) 21141
 
5.9%
Common
ValueCountFrequency (%)
0 603521
28.9%
1 303501
14.5%
2 197610
 
9.5%
3 137382
 
6.6%
5 128600
 
6.2%
4 125686
 
6.0%
6 115089
 
5.5%
108568
 
5.2%
8 108126
 
5.2%
7 104831
 
5.0%
Other values (4) 156153
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2449832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 603521
24.6%
1 303501
12.4%
2 197610
 
8.1%
3 137382
 
5.6%
5 128600
 
5.2%
4 125686
 
5.1%
N 124882
 
5.1%
S 118677
 
4.8%
6 115089
 
4.7%
108568
 
4.4%
Other values (44) 486316
19.9%

CD_ORIGEM_DESPESA
Real number (ℝ)

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20237628
Minimum20010000
Maximum20900000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:41.308287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum20010000
5-th percentile20010000
Q120090000
median20140000
Q320210000
95-th percentile20800000
Maximum20900000
Range890000
Interquartile range (IQR)120000

Descriptive statistics

Standard deviation269873.1
Coefficient of variation (CV)0.013335214
Kurtosis0.31940438
Mean20237628
Median Absolute Deviation (MAD)70000
Skewness1.3641685
Sum1.1898916 × 1013
Variance7.2831488 × 1010
MonotonicityNot monotonic
2022-12-06T13:12:41.411270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
20010000 139290
23.7%
20140000 113454
19.3%
20800000 98250
16.7%
20210000 61515
10.5%
20110000 47402
 
8.1%
20120000 38942
 
6.6%
20100000 27814
 
4.7%
20420000 10099
 
1.7%
20270000 8242
 
1.4%
20360002 5062
 
0.9%
Other values (31) 37890
 
6.4%
ValueCountFrequency (%)
20010000 139290
23.7%
20020000 135
 
< 0.1%
20030000 519
 
0.1%
20040000 2271
 
0.4%
20050000 1036
 
0.2%
20060000 683
 
0.1%
20070000 630
 
0.1%
20080000 924
 
0.2%
20090000 2513
 
0.4%
20100000 27814
 
4.7%
ValueCountFrequency (%)
20900000 10
 
< 0.1%
20800000 98250
16.7%
20600000 4861
 
0.8%
20430000 27
 
< 0.1%
20420000 10099
 
1.7%
20410000 2191
 
0.4%
20400000 74
 
< 0.1%
20360002 5062
 
0.9%
20360001 4572
 
0.8%
20360000 1297
 
0.2%
Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.4 MiB
Despesas com pessoal
139290 
Publicidade por materiais impressos
113454 
Atividades de militância e mobilização de rua
98250 
Encargos financeiros, taxas bancárias e/ou op. cartão de crédito
61515 
Publicidade por adesivos
47402 
Other values (36)
128049 

Length

Max length64
Median length48
Mean length34.257633
Min length4

Characters and Unicode

Total characters20142118
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublicidade por materiais impressos
2nd rowPublicidade por materiais impressos
3rd rowPublicidade por materiais impressos
4th rowPublicidade por materiais impressos
5th rowPublicidade por materiais impressos

Common Values

ValueCountFrequency (%)
Despesas com pessoal 139290
23.7%
Publicidade por materiais impressos 113454
19.3%
Atividades de militância e mobilização de rua 98250
16.7%
Encargos financeiros, taxas bancárias e/ou op. cartão de crédito 61515
10.5%
Publicidade por adesivos 47402
 
8.1%
Serviços prestados por terceiros 38942
 
6.6%
Combustíveis e lubrificantes 27814
 
4.7%
Despesa com Impulsionamento de Conteúdos 10099
 
1.7%
Diversas a especificar 8242
 
1.4%
Serviços contábeis 5062
 
0.9%
Other values (31) 37890
 
6.4%

Length

2022-12-06T13:12:41.542849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 293099
 
10.7%
por 203699
 
7.4%
publicidade 163436
 
6.0%
com 150642
 
5.5%
despesas 141440
 
5.2%
pessoal 139290
 
5.1%
e 133728
 
4.9%
materiais 115967
 
4.2%
impressos 113454
 
4.1%
atividades 98250
 
3.6%
Other values (95) 1189689
43.4%

Most occurring characters

ValueCountFrequency (%)
2154734
10.7%
s 2003381
 
9.9%
i 1928622
 
9.6%
e 1873917
 
9.3%
a 1734535
 
8.6%
o 1533709
 
7.6%
r 1090660
 
5.4%
d 1005336
 
5.0%
c 847723
 
4.2%
p 743285
 
3.7%
Other values (44) 5226216
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17176085
85.3%
Space Separator 2154734
 
10.7%
Uppercase Letter 615057
 
3.1%
Other Punctuation 194908
 
1.0%
Open Punctuation 630
 
< 0.1%
Close Punctuation 630
 
< 0.1%
Dash Punctuation 74
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 2003381
11.7%
i 1928622
11.2%
e 1873917
10.9%
a 1734535
10.1%
o 1533709
8.9%
r 1090660
 
6.3%
d 1005336
 
5.9%
c 847723
 
4.9%
p 743285
 
4.3%
t 700218
 
4.1%
Other values (23) 3714699
21.6%
Uppercase Letter
ValueCountFrequency (%)
P 168479
27.4%
D 160962
26.2%
A 104654
17.0%
E 62516
 
10.2%
S 49873
 
8.1%
C 47567
 
7.7%
I 10618
 
1.7%
L 2901
 
0.5%
M 2518
 
0.4%
T 2267
 
0.4%
Other values (4) 2702
 
0.4%
Other Punctuation
ValueCountFrequency (%)
, 66899
34.3%
/ 66494
34.1%
. 61515
31.6%
Space Separator
ValueCountFrequency (%)
2154734
100.0%
Open Punctuation
ValueCountFrequency (%)
( 630
100.0%
Close Punctuation
ValueCountFrequency (%)
) 630
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17791142
88.3%
Common 2350976
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 2003381
11.3%
i 1928622
10.8%
e 1873917
10.5%
a 1734535
9.7%
o 1533709
 
8.6%
r 1090660
 
6.1%
d 1005336
 
5.7%
c 847723
 
4.8%
p 743285
 
4.2%
t 700218
 
3.9%
Other values (37) 4329756
24.3%
Common
ValueCountFrequency (%)
2154734
91.7%
, 66899
 
2.8%
/ 66494
 
2.8%
. 61515
 
2.6%
( 630
 
< 0.1%
) 630
 
< 0.1%
- 74
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19488446
96.8%
None 653672
 
3.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2154734
11.1%
s 2003381
10.3%
i 1928622
9.9%
e 1873917
9.6%
a 1734535
 
8.9%
o 1533709
 
7.9%
r 1090660
 
5.6%
d 1005336
 
5.2%
c 847723
 
4.3%
p 743285
 
3.8%
Other values (33) 4572544
23.5%
None
ValueCountFrequency (%)
ã 192787
29.5%
ç 172145
26.3%
â 98250
15.0%
á 70286
 
10.8%
é 61719
 
9.4%
í 40170
 
6.1%
ú 10099
 
1.5%
ó 4450
 
0.7%
õ 2471
 
0.4%
ê 998
 
0.2%

SQ_DESPESA
Real number (ℝ)

Distinct462837
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38404525
Minimum17564736
Maximum50983242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:41.680957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum17564736
5-th percentile23983414
Q125330498
median39974406
Q348654516
95-th percentile50454768
Maximum50983242
Range33418506
Interquartile range (IQR)23324018

Descriptive statistics

Standard deviation9793780.8
Coefficient of variation (CV)0.25501632
Kurtosis-1.2730769
Mean38404525
Median Absolute Deviation (MAD)8872879
Skewness-0.33896508
Sum2.2580325 × 1013
Variance9.5918142 × 1013
MonotonicityNot monotonic
2022-12-06T13:12:41.796870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47612881 220
 
< 0.1%
40036858 200
 
< 0.1%
40037055 190
 
< 0.1%
40036341 184
 
< 0.1%
47612912 157
 
< 0.1%
49562966 147
 
< 0.1%
39974729 147
 
< 0.1%
50271323 147
 
< 0.1%
50923814 134
 
< 0.1%
41022046 134
 
< 0.1%
Other values (462827) 586300
99.7%
ValueCountFrequency (%)
17564736 2
< 0.1%
17564737 1
 
< 0.1%
17590916 3
< 0.1%
17597293 3
< 0.1%
17597294 3
< 0.1%
17597295 1
 
< 0.1%
17609525 1
 
< 0.1%
17621322 1
 
< 0.1%
17621323 1
 
< 0.1%
17630219 2
< 0.1%
ValueCountFrequency (%)
50983242 1
< 0.1%
50983241 1
< 0.1%
50983240 1
< 0.1%
50983239 1
< 0.1%
50983238 1
< 0.1%
50983237 1
< 0.1%
50983236 1
< 0.1%
50983235 1
< 0.1%
50983234 1
< 0.1%
50983233 1
< 0.1%

DT_DESPESA
Categorical

Distinct494
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size37.6 MiB
01/09/2022
 
19049
16/08/2022
 
13966
09/09/2022
 
12329
10/09/2018
 
11426
13/11/2020
 
10923
Other values (489)
520267 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5879600
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)< 0.1%

Sample

1st row30/09/2018
2nd row30/09/2018
3rd row30/09/2018
4th row30/09/2018
5th row30/09/2018

Common Values

ValueCountFrequency (%)
01/09/2022 19049
 
3.2%
16/08/2022 13966
 
2.4%
09/09/2022 12329
 
2.1%
10/09/2018 11426
 
1.9%
13/11/2020 10923
 
1.9%
02/09/2022 10719
 
1.8%
21/10/2020 9647
 
1.6%
15/10/2020 8756
 
1.5%
09/11/2020 8278
 
1.4%
26/10/2020 8264
 
1.4%
Other values (484) 474603
80.7%

Length

2022-12-06T13:12:41.912413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01/09/2022 19049
 
3.2%
16/08/2022 13966
 
2.4%
09/09/2022 12329
 
2.1%
10/09/2018 11426
 
1.9%
13/11/2020 10923
 
1.9%
02/09/2022 10719
 
1.8%
21/10/2020 9647
 
1.6%
15/10/2020 8756
 
1.5%
09/11/2020 8278
 
1.4%
26/10/2020 8264
 
1.4%
Other values (484) 474603
80.7%

Most occurring characters

ValueCountFrequency (%)
0 1577450
26.8%
2 1437542
24.4%
/ 1175920
20.0%
1 783508
13.3%
9 318382
 
5.4%
8 281112
 
4.8%
3 85924
 
1.5%
6 74169
 
1.3%
5 54335
 
0.9%
4 47314
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4703680
80.0%
Other Punctuation 1175920
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1577450
33.5%
2 1437542
30.6%
1 783508
16.7%
9 318382
 
6.8%
8 281112
 
6.0%
3 85924
 
1.8%
6 74169
 
1.6%
5 54335
 
1.2%
4 47314
 
1.0%
7 43944
 
0.9%
Other Punctuation
ValueCountFrequency (%)
/ 1175920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5879600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1577450
26.8%
2 1437542
24.4%
/ 1175920
20.0%
1 783508
13.3%
9 318382
 
5.4%
8 281112
 
4.8%
3 85924
 
1.5%
6 74169
 
1.3%
5 54335
 
0.9%
4 47314
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5879600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1577450
26.8%
2 1437542
24.4%
/ 1175920
20.0%
1 783508
13.3%
9 318382
 
5.4%
8 281112
 
4.8%
3 85924
 
1.5%
6 74169
 
1.3%
5 54335
 
0.9%
4 47314
 
0.8%

DS_DESPESA
Categorical

Distinct192977
Distinct (%)32.9%
Missing1905
Missing (%)0.3%
Memory size53.9 MiB
PANFLETAGEM
 
14368
TARIFA BANCARIA
 
9811
GASOLINA COMUM
 
6854
CABO ELEITORAL
 
6746
DIVULGAÇÃO DE CAMPANHA
 
4173
Other values (192972)
544103 

Length

Max length65
Median length45
Mean length29.226177
Min length1

Characters and Unicode

Total characters17128147
Distinct characters93
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique169318 ?
Unique (%)28.9%

Sample

1st rowNF 156086 SANTINHO 7X10 - MARCELO SIMÃO E APOIO
2nd rowNF 156085 - CARTÕES 5X9 - MARCELO SIMÃO E APOIO
3rd rowNF 156083 SANTINHO 7X10 - MARCELO SIMÃO/CLARISSA
4th rowNF 155496 PRAGIONHA 10X10 MARCELO SIMÃO E APOIO
5th rowNF 1561130 - PAPEL CARTÃO 29X40 MARCELO SIMÃO

Common Values

ValueCountFrequency (%)
PANFLETAGEM 14368
 
2.4%
TARIFA BANCARIA 9811
 
1.7%
GASOLINA COMUM 6854
 
1.2%
CABO ELEITORAL 6746
 
1.1%
DIVULGAÇÃO DE CAMPANHA 4173
 
0.7%
TARIFA BANCÁRIA 3384
 
0.6%
MILITANCIA 2908
 
0.5%
PRESTAÇÃO DE SERVIÇOS PARA CAMPANHA ELEITORAL 2778
 
0.5%
TARIFA DOC/TED ELETRONICO 2609
 
0.4%
DESPESA COM PESSOAL DE CAMPANHA 2296
 
0.4%
Other values (192967) 530128
90.2%

Length

2022-12-06T13:12:42.081697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 266863
 
10.4%
campanha 73767
 
2.9%
eleitoral 57035
 
2.2%
a 54409
 
2.1%
e 53342
 
2.1%
serviços 50025
 
2.0%
49338
 
1.9%
panfletagem 44595
 
1.7%
tarifa 43564
 
1.7%
serviço 36914
 
1.4%
Other values (75923) 1825368
71.4%

Most occurring characters

ValueCountFrequency (%)
2029872
 
11.9%
A 1886258
 
11.0%
E 1403229
 
8.2%
O 1173956
 
6.9%
I 1079402
 
6.3%
R 922894
 
5.4%
D 746954
 
4.4%
S 714472
 
4.2%
N 691368
 
4.0%
T 661606
 
3.9%
Other values (83) 5818136
34.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13407149
78.3%
Space Separator 2029872
 
11.9%
Decimal Number 1262507
 
7.4%
Other Punctuation 270385
 
1.6%
Lowercase Letter 74460
 
0.4%
Dash Punctuation 65643
 
0.4%
Math Symbol 5379
 
< 0.1%
Open Punctuation 4435
 
< 0.1%
Close Punctuation 4303
 
< 0.1%
Other Letter 2692
 
< 0.1%
Other values (3) 1322
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1886258
14.1%
E 1403229
 
10.5%
O 1173956
 
8.8%
I 1079402
 
8.1%
R 922894
 
6.9%
D 746954
 
5.6%
S 714472
 
5.3%
N 691368
 
5.2%
T 661606
 
4.9%
C 562723
 
4.2%
Other values (29) 3564287
26.6%
Lowercase Letter
ValueCountFrequency (%)
a 10950
14.7%
i 10950
14.7%
o 8760
11.8%
n 8760
11.8%
t 6570
8.8%
e 4380
 
5.9%
m 4380
 
5.9%
s 2190
 
2.9%
c 2190
 
2.9%
ã 2190
 
2.9%
Other values (6) 13140
17.6%
Other Punctuation
ValueCountFrequency (%)
/ 164971
61.0%
· 53335
 
19.7%
. 49494
 
18.3%
: 1501
 
0.6%
' 583
 
0.2%
, 250
 
0.1%
* 125
 
< 0.1%
! 53
 
< 0.1%
% 34
 
< 0.1%
# 17
 
< 0.1%
Other values (3) 22
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 363243
28.8%
1 243371
19.3%
2 132455
 
10.5%
5 103825
 
8.2%
4 103808
 
8.2%
9 89451
 
7.1%
8 69546
 
5.5%
3 58056
 
4.6%
6 50166
 
4.0%
7 48586
 
3.8%
Math Symbol
ValueCountFrequency (%)
+ 5267
97.9%
= 93
 
1.7%
| 19
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 4278
99.4%
] 23
 
0.5%
} 2
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 4417
99.6%
[ 18
 
0.4%
Other Letter
ValueCountFrequency (%)
º 1574
58.5%
ª 1118
41.5%
Space Separator
ValueCountFrequency (%)
2029872
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65643
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 911
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 274
100.0%
Other Number
ValueCountFrequency (%)
² 137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13484301
78.7%
Common 3643846
 
21.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1886258
14.0%
E 1403229
 
10.4%
O 1173956
 
8.7%
I 1079402
 
8.0%
R 922894
 
6.8%
D 746954
 
5.5%
S 714472
 
5.3%
N 691368
 
5.1%
T 661606
 
4.9%
C 562723
 
4.2%
Other values (47) 3641439
27.0%
Common
ValueCountFrequency (%)
2029872
55.7%
0 363243
 
10.0%
1 243371
 
6.7%
/ 164971
 
4.5%
2 132455
 
3.6%
5 103825
 
2.8%
4 103808
 
2.8%
9 89451
 
2.5%
8 69546
 
1.9%
- 65643
 
1.8%
Other values (26) 277661
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16652111
97.2%
None 476036
 
2.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2029872
 
12.2%
A 1886258
 
11.3%
E 1403229
 
8.4%
O 1173956
 
7.0%
I 1079402
 
6.5%
R 922894
 
5.5%
D 746954
 
4.5%
S 714472
 
4.3%
N 691368
 
4.2%
T 661606
 
4.0%
Other values (64) 5342100
32.1%
None
ValueCountFrequency (%)
Ç 213292
44.8%
à 141531
29.7%
· 53335
 
11.2%
Á 11872
 
2.5%
 11544
 
2.4%
Í 10359
 
2.2%
Õ 8475
 
1.8%
É 4862
 
1.0%
Ô 3837
 
0.8%
Ê 3484
 
0.7%
Other values (9) 13445
 
2.8%

VR_DESPESA_CONTRATADA
Real number (ℝ)

HIGH CORRELATION
SKEWED

Distinct26790
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502.6668
Minimum0.01
Maximum6800000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2022-12-06T13:12:42.250084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile10.45
Q1150
median500
Q31045
95-th percentile4600
Maximum6800000
Range6800000
Interquartile range (IQR)895

Descriptive statistics

Standard deviation18933.812
Coefficient of variation (CV)12.600139
Kurtosis51723.769
Mean1502.6668
Median Absolute Deviation (MAD)420
Skewness195.05302
Sum8.83508 × 108
Variance3.5848922 × 108
MonotonicityNot monotonic
2022-12-06T13:12:42.381809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000.0 31145
 
5.3%
500.0 28103
 
4.8%
600.0 22074
 
3.8%
300.0 20224
 
3.4%
250.0 14631
 
2.5%
1500.0 13851
 
2.4%
1200.0 13546
 
2.3%
400.0 12954
 
2.2%
200.0 12755
 
2.2%
800.0 9926
 
1.7%
Other values (26780) 408751
69.5%
ValueCountFrequency (%)
0.01 63
< 0.1%
0.02 31
< 0.1%
0.03 31
< 0.1%
0.04 33
< 0.1%
0.05 29
< 0.1%
0.06 24
 
< 0.1%
0.07 8
 
< 0.1%
0.08 14
 
< 0.1%
0.09 10
 
< 0.1%
0.1 32
< 0.1%
ValueCountFrequency (%)
6800000.0 1
< 0.1%
4500000.0 2
< 0.1%
4260799.8 2
< 0.1%
3500000.0 1
< 0.1%
2350000.0 2
< 0.1%
2110000.0 1
< 0.1%
1938500.0 1
< 0.1%
1900000.0 1
< 0.1%
1500000.0 2
< 0.1%
1450000.0 1
< 0.1%

Interactions

2022-12-06T13:11:57.936273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:35.957615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:44.437598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:52.295099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-06T13:11:05.249540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-06T13:11:13.868299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-06T13:11:54.074267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:58.516917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-06T13:11:18.233722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-06T13:11:30.640219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:34.946006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:38.712181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:40.967842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:42.994732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:44.965862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:46.992090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:50.265882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:54.384024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:58.783764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:36.981690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:47.625734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-06T13:11:06.155159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:10.736012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:14.744571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:18.367832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:21.204933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:31.407953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:35.201300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:38.829077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:41.082664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:43.099163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:45.075281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:47.105712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:50.521827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:54.650697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:59.086495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:37.285170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:47.931624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:54.861688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:02.421774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-06T13:11:11.019530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-06T13:11:18.518346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:21.461952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:31.603703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:35.478747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:38.940696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:41.181364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:43.193163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:45.170489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-06T13:11:12.830723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-06T13:11:19.788265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:24.342891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:33.130629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:37.594048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:40.240733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:42.354333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:44.326145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:46.316329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:48.545941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:52.697143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:56.861164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:12:01.267212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:42.253159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:51.363082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:00.561549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:04.450999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:08.738484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:13.085372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:17.278110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:19.921724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:25.703593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:33.751297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:37.859424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:40.364046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:42.465126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:44.439777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:46.431189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:48.661991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:52.971473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:57.133558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:12:01.528733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:42.526681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:51.650355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:00.849700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:04.717077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:09.033089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:13.337940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:17.565213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:20.046308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:27.056501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:33.932185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:38.105082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:40.481421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:42.570484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:44.549682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:46.549126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:49.302796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:53.225215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:57.405366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:12:01.797775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:44.138053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:10:51.930724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:01.134392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:04.949150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:09.317570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:13.568053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:17.828111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:20.166233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:27.573999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:34.112608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:38.361237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:40.603188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:42.674545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:44.655534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:46.654245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:49.411372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:53.489511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-06T13:11:57.668477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-06T13:12:42.630765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-06T13:12:43.014841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-06T13:12:43.284930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-06T13:12:43.563902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-06T13:12:43.846716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-06T13:12:44.102700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-06T13:12:05.504612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-06T13:12:09.348347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-06T13:12:15.428695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ANO_ELEICAOCD_TIPO_ELEICAONM_TIPO_ELEICAOCD_ELEICAODS_ELEICAODT_ELEICAOST_TURNOTP_PRESTACAO_CONTASDT_PRESTACAO_CONTASSQ_PRESTADOR_CONTASSG_UFSG_UENM_UENR_CNPJ_PRESTADOR_CONTACD_CARGODS_CARGOSQ_CANDIDATONR_CANDIDATONM_CANDIDATONR_CPF_CANDIDATONR_CPF_VICE_CANDIDATONR_PARTIDOSG_PARTIDONM_PARTIDOCD_TIPO_FORNECEDORDS_TIPO_FORNECEDORCD_CNAE_FORNECEDORDS_CNAE_FORNECEDORNR_CPF_CNPJ_FORNECEDORNM_FORNECEDORNM_FORNECEDOR_RFBCD_ESFERA_PART_FORNECEDORDS_ESFERA_PART_FORNECEDORSG_UF_FORNECEDORCD_MUNICIPIO_FORNECEDORNM_MUNICIPIO_FORNECEDORSQ_CANDIDATO_FORNECEDORNR_CANDIDATO_FORNECEDORCD_CARGO_FORNECEDORDS_CARGO_FORNECEDORNR_PARTIDO_FORNECEDORSG_PARTIDO_FORNECEDORNM_PARTIDO_FORNECEDORDS_TIPO_DOCUMENTONR_DOCUMENTOCD_ORIGEM_DESPESADS_ORIGEM_DESPESASQ_DESPESADT_DESPESADS_DESPESAVR_DESPESA_CONTRATADA
020182Ordinária297Eleições Gerais Estaduais 201807/10/20181Final05/12/2018416623447RJRJRIO DE JANEIRO311353990001417Deputado Estadual19000060137111011MARCELO NASCIF SIMÃO70261466704<NA>11PPProgressistas1Pessoa Jurídica58115Edição de livros14572530000119.0APEL GRÁFICA E EDITORA LTDAAPEL GRAFICA E EDITORA LTDA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal155496 - 120140000Publicidade por materiais impressos2458842830/09/2018NF 156086 SANTINHO 7X10 - MARCELO SIMÃO E APOIO300.0
120182Ordinária297Eleições Gerais Estaduais 201807/10/20181Final05/12/2018416623447RJRJRIO DE JANEIRO311353990001417Deputado Estadual19000060137111011MARCELO NASCIF SIMÃO70261466704<NA>11PPProgressistas1Pessoa Jurídica58115Edição de livros14572530000119.0APEL GRÁFICA E EDITORA LTDAAPEL GRAFICA E EDITORA LTDA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal155496 - 120140000Publicidade por materiais impressos2458842830/09/2018NF 156085 - CARTÕES 5X9 - MARCELO SIMÃO E APOIO31.6
220182Ordinária297Eleições Gerais Estaduais 201807/10/20181Final05/12/2018416623447RJRJRIO DE JANEIRO311353990001417Deputado Estadual19000060137111011MARCELO NASCIF SIMÃO70261466704<NA>11PPProgressistas1Pessoa Jurídica58115Edição de livros14572530000119.0APEL GRÁFICA E EDITORA LTDAAPEL GRAFICA E EDITORA LTDA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal155496 - 120140000Publicidade por materiais impressos2458842830/09/2018NF 156083 SANTINHO 7X10 - MARCELO SIMÃO/CLARISSA300.0
320182Ordinária297Eleições Gerais Estaduais 201807/10/20181Final05/12/2018416623447RJRJRIO DE JANEIRO311353990001417Deputado Estadual19000060137111011MARCELO NASCIF SIMÃO70261466704<NA>11PPProgressistas1Pessoa Jurídica58115Edição de livros14572530000119.0APEL GRÁFICA E EDITORA LTDAAPEL GRAFICA E EDITORA LTDA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal155496 - 120140000Publicidade por materiais impressos2458842830/09/2018NF 155496 PRAGIONHA 10X10 MARCELO SIMÃO E APOIO1998.0
420182Ordinária297Eleições Gerais Estaduais 201807/10/20181Final05/12/2018416623447RJRJRIO DE JANEIRO311353990001417Deputado Estadual19000060137111011MARCELO NASCIF SIMÃO70261466704<NA>11PPProgressistas1Pessoa Jurídica58115Edição de livros14572530000119.0APEL GRÁFICA E EDITORA LTDAAPEL GRAFICA E EDITORA LTDA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal155496 - 120140000Publicidade por materiais impressos2458842830/09/2018NF 1561130 - PAPEL CARTÃO 29X40 MARCELO SIMÃO100.0
520182Ordinária297Eleições Gerais Estaduais 201807/10/20181Final19/12/2018419051020RJRJRIO DE JANEIRO311670430001906Deputado Federal1900006034264511RODRIGO FERREIRA DE MENDONÇA7906860748<NA>45PSDBPartido da Social Democracia Brasileira0Pessoa Física<NA><NA>14822772713.0JHENYFER MARINHO DE OLIVEIRAJHENYFER MARINHO DE OLIVEIRA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>OutroSN20120000Serviços prestados por terceiros2477219622/09/2018CONTRATO DE PRESTAÇÃO DE SERVIÇO DE PANFLETAGEM300.0
620182Ordinária297Eleições Gerais Estaduais 201807/10/20181Final21/02/2019416869997RJRJRIO DE JANEIRO311435350001456Deputado Federal1900006021175055RENATO ATHAYDE SILVA1485023777<NA>50PSOLPartido Socialismo e Liberdade1Pessoa Jurídica18113Impressão de jornais, livros, revistas e outras publicações periódicas30383720000144.0MCE COMERCIO EDITORIAL EIRELIMCE COMERCIO EDITORIAL EIRELI<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal259 - 120140000Publicidade por materiais impressos2492922512/09/2018NF 259 - 20.000 SANTINHOS CINCO-ANDRE390.56
720182Ordinária297Eleições Gerais Estaduais 201807/10/20181Final23/11/2018419492536RJRJRIO DE JANEIRO311812040001087Deputado Estadual19000060618631615VALDECIR DIAS DA SILVA1397326751<NA>31PHSPartido Humanista da Solidariedade0Pessoa Física<NA><NA>10235359718.0CAMILA CRISTINA TORRES GUIMARAESCAMILA CRISTINA TORRES GUIMARAES<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Recibo2320120000Serviços prestados por terceiros2419325201/09/2018PRESTAÇÃO DE SERVIÇO DE DIVULGAÇÃO DE CAMPANHA300.0
820182Ordinária297Eleições Gerais Estaduais 201807/10/20181Final23/11/2018419492536RJRJRIO DE JANEIRO311812040001087Deputado Estadual19000060618631615VALDECIR DIAS DA SILVA1397326751<NA>31PHSPartido Humanista da Solidariedade0Pessoa Física<NA><NA>11479589713.0GRACIELE DA SILVA MILITAOGRACIELE DA SILVA MILITAO<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Recibo3920120000Serviços prestados por terceiros2419329801/09/2018PRESTAÇÃO DE SERVIÇO DE DIVULGAÇÃO DE CAMPANHA300.0
920182Ordinária297Eleições Gerais Estaduais 201807/10/20181Final23/11/2018419492536RJRJRIO DE JANEIRO311812040001087Deputado Estadual19000060618631615VALDECIR DIAS DA SILVA1397326751<NA>31PHSPartido Humanista da Solidariedade0Pessoa Física<NA><NA>3037769785.0CIRLEIA DE MATTOS BORGES VAZCIRLEIA DE MATTOS BORGES VAZ<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Recibo4520120000Serviços prestados por terceiros2419325501/09/2018PRESTAÇÃO DE SERVIÇO DE DIVULGAÇÃO DE CAMPANHA300.0
ANO_ELEICAOCD_TIPO_ELEICAONM_TIPO_ELEICAOCD_ELEICAODS_ELEICAODT_ELEICAOST_TURNOTP_PRESTACAO_CONTASDT_PRESTACAO_CONTASSQ_PRESTADOR_CONTASSG_UFSG_UENM_UENR_CNPJ_PRESTADOR_CONTACD_CARGODS_CARGOSQ_CANDIDATONR_CANDIDATONM_CANDIDATONR_CPF_CANDIDATONR_CPF_VICE_CANDIDATONR_PARTIDOSG_PARTIDONM_PARTIDOCD_TIPO_FORNECEDORDS_TIPO_FORNECEDORCD_CNAE_FORNECEDORDS_CNAE_FORNECEDORNR_CPF_CNPJ_FORNECEDORNM_FORNECEDORNM_FORNECEDOR_RFBCD_ESFERA_PART_FORNECEDORDS_ESFERA_PART_FORNECEDORSG_UF_FORNECEDORCD_MUNICIPIO_FORNECEDORNM_MUNICIPIO_FORNECEDORSQ_CANDIDATO_FORNECEDORNR_CANDIDATO_FORNECEDORCD_CARGO_FORNECEDORDS_CARGO_FORNECEDORNR_PARTIDO_FORNECEDORSG_PARTIDO_FORNECEDORNM_PARTIDO_FORNECEDORDS_TIPO_DOCUMENTONR_DOCUMENTOCD_ORIGEM_DESPESADS_ORIGEM_DESPESASQ_DESPESADT_DESPESADS_DESPESAVR_DESPESA_CONTRATADA
58795020222Ordinária546Eleições Gerais Estaduais 202202/10/20221Final27/10/20223797176489RJRJRIO DE JANEIRO475550430001057Deputado Estadual19000165422412100CARLOS ALBERTO CACAU DE BRITO8778957591<NA>12PDTPartido Democrático Trabalhista1Pessoa Jurídica59120Atividades de pós-produção cinematográfica, de vídeos e de programas de televisão37171479000139.0KESIA DA COSTA KAFFARO NOVAESKESIA DA COSTA CAFFARO NOVAES 10528631756<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal0000000920220000Produção de programas de rádio, televisão ou vídeo4787816726/09/2022S FILMAGENS:REUNIÕES CAMPANHA·CAMINHADAS ELET FOTO3000.0
58795120222Ordinária546Eleições Gerais Estaduais 202202/10/20221Final21/10/20223787878844RJRJRIO DE JANEIRO475103280001117Deputado Estadual19000162049965656LILIAN PRATES BELEM BEHRING1122372744<NA>65PC do BPartido Comunista do Brasil1Pessoa Jurídica59120Atividades de pós-produção cinematográfica, de vídeos e de programas de televisão37171479000139.0KESIA DA COSTA CAFFARO NOVAES 10529631756KESIA DA COSTA CAFFARO NOVAES 10528631756<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal0000000820200000Eventos de promoção da candidatura4714444226/09/2022SERVIÇOS DE PO·OÇÃO DA CANDIDATURA2500.0
58795220222Ordinária546Eleições Gerais Estaduais 202202/10/20221Final01/11/20223778707514RJRJRIO DE JANEIRO474641550001426Deputado Federal1900016096841998LEANDRO NASCIMENTO FARIAS12259023711<NA>19PODEPodemos1Pessoa Jurídica59120Atividades de pós-produção cinematográfica, de vídeos e de programas de televisão37171479000139.0KESIA DA COSTA CAFFARO NOVAESKESIA DA COSTA CAFFARO NOVAES 10528631756<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal620220000Produção de programas de rádio, televisão ou vídeo4889955005/09/2022SERVIÇO DE FILMAGEM10000.0
58795320222Ordinária546Eleições Gerais Estaduais 202202/10/20221Final01/11/20223778707514RJRJRIO DE JANEIRO474641550001426Deputado Federal1900016096841998LEANDRO NASCIMENTO FARIAS12259023711<NA>19PODEPodemos1Pessoa Jurídica59120Atividades de pós-produção cinematográfica, de vídeos e de programas de televisão37171479000139.0KESIA DA COSTA CAFFARO NOVAESKESIA DA COSTA CAFFARO NOVAES 10528631756<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal720220000Produção de programas de rádio, televisão ou vídeo4889955512/09/2022SERVIÇO DE FILMAGEM2700.0
58795420222Ordinária546Eleições Gerais Estaduais 202202/10/20221Final28/10/20223796006374RJRJRIO DE JANEIRO475519510001127Deputado Estadual19000164958451314ADRIANA DE MORAES DE OLIVEIRA4190128775<NA>51PATRIOTAPatriota1Pessoa Jurídica58298Edição integrada à impressão de cadastros, listas e outros produtos gráficos30298755000185.0MAXMILIANO MARIA KOLBE SERVICOS GRAFICOSMAXIMILIANO MARIA KOLBE SERVICOS GRAFICOS EIRELI<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal14620140000Publicidade por materiais impressos4830884430/09/2022FOLHETOS 10X14 CM 4/4 COUCHET 90G990.0
58795520222Ordinária546Eleições Gerais Estaduais 202202/10/20221Final04/11/20223781004719RJRJRIO DE JANEIRO474746870001607Deputado Estadual19000161222255622CLAUDIO SOBRAL DE CAIADO CASTRO JUNIOR5581998795<NA>55PSDPartido Social Democrático1Pessoa Jurídica85112Educação infantil - creche40375008000157.0CENTRO EDUCACIONAL BARROS MARTINS (GOLÉGIO GIRASOL)CENTRO EDUCACIONAL BARROS MARTINS LTDA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Recibo00120040000Locação/cessão de bens imóveis5024241425/08/2022LOCAÇÃO DE ESPAÇO PARA REUNIÃO DE CAMPANHA500.0
58795620222Ordinária546Eleições Gerais Estaduais 202202/10/20221Final01/11/20223754137317RJRJRIO DE JANEIRO473689930001123Governador19000160039930PAULO GUSTAVO GANIME ALVES TEIXEIRA991196775110643846760.030NOVOPartido Novo1Pessoa Jurídica74200Atividades fotográficas e similares32120563000100.0LUCIANA RODRIGUES FERREIRA 05436924778LUCIANA RODRIGUES FERREIRA 05436924778<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal0000003520120000Serviços prestados por terceiros4956121631/08/2022FOTOGRAFIAS E VIDEOS PARA CAMPANHA12452.0
58795720222Ordinária546Eleições Gerais Estaduais 202202/10/20221Final01/11/20223754137317RJRJRIO DE JANEIRO473689930001123Governador19000160039930PAULO GUSTAVO GANIME ALVES TEIXEIRA991196775110643846760.030NOVOPartido Novo1Pessoa Jurídica74200Atividades fotográficas e similares32120563000100.0LUCIANA RODRIGUES FERREIRA 05436924778LUCIANA RODRIGUES FERREIRA 05436924778<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal0000003620120000Serviços prestados por terceiros4956124331/08/2022FOTOGRAFIAS E VIDEOS PARA CAMPANHA DO GOV.7565.0
58795820222Ordinária546Eleições Gerais Estaduais 202202/10/20221Final31/10/20223778706789RJRJRIO DE JANEIRO474637860001476Deputado Federal1900016096601918ADRIANO DE ALMEIDA SILVA1913073785<NA>19PODEPodemos1Pessoa Jurídica59120Atividades de pós-produção cinematográfica, de vídeos e de programas de televisão40273177000186.0FERNANDA DE CASTRO CASALLIFERNANDA DE CASTRO CASALLI 10947217789<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal4920220000Produção de programas de rádio, televisão ou vídeo4874583720/09/2022PRODUÇÃO DE PROGRAMA ELEITORAL15000.0
58795920222Ordinária546Eleições Gerais Estaduais 202202/10/20221Final31/10/20223778706789RJRJRIO DE JANEIRO474637860001476Deputado Federal1900016096601918ADRIANO DE ALMEIDA SILVA1913073785<NA>19PODEPodemos1Pessoa Jurídica59120Atividades de pós-produção cinematográfica, de vídeos e de programas de televisão40273177000186.0FERNANDA DE CASTRO CASALLIFERNANDA DE CASTRO CASALLI 10947217789<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal4820220000Produção de programas de rádio, televisão ou vídeo4874584120/09/2022CAPTAÇÃO DE IMAGEM PARA PRODUÇÃO10000.0

Duplicate rows

Most frequently occurring

ANO_ELEICAOCD_TIPO_ELEICAONM_TIPO_ELEICAOCD_ELEICAODS_ELEICAODT_ELEICAOST_TURNOTP_PRESTACAO_CONTASDT_PRESTACAO_CONTASSQ_PRESTADOR_CONTASSG_UFNM_UENR_CNPJ_PRESTADOR_CONTACD_CARGODS_CARGOSQ_CANDIDATONR_CANDIDATONM_CANDIDATONR_CPF_CANDIDATONR_CPF_VICE_CANDIDATONR_PARTIDOSG_PARTIDONM_PARTIDOCD_TIPO_FORNECEDORDS_TIPO_FORNECEDORCD_CNAE_FORNECEDORDS_CNAE_FORNECEDORNR_CPF_CNPJ_FORNECEDORNM_FORNECEDORNM_FORNECEDOR_RFBSG_UF_FORNECEDORCD_MUNICIPIO_FORNECEDORNM_MUNICIPIO_FORNECEDORSQ_CANDIDATO_FORNECEDORNR_CANDIDATO_FORNECEDORCD_CARGO_FORNECEDORDS_CARGO_FORNECEDORNR_PARTIDO_FORNECEDORSG_PARTIDO_FORNECEDORNM_PARTIDO_FORNECEDORDS_TIPO_DOCUMENTONR_DOCUMENTOCD_ORIGEM_DESPESADS_ORIGEM_DESPESASQ_DESPESADT_DESPESADS_DESPESAVR_DESPESA_CONTRATADA# duplicates
359020202Ordinária426Eleições Municipais 202015/11/20201Final27/01/20211828967198RJNILÓPOLIS3851094100014811Prefeito19000064976122ABRAAO DAVID NETO53060407805620068773.022PLPartido Liberal1Pessoa Jurídica47318Comércio varejista de combustíveis para veículos automotores40438186000180.0AUTO POSTO NILOPOLIS LTDA MEAUTO POSTO NILOPOLIS LTDA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Recibo220100000Combustíveis e lubrificantes4003685813/10/2020ETANOL20.0114
360620202Ordinária426Eleições Municipais 202015/11/20201Final27/01/20211828967198RJNILÓPOLIS3851094100014811Prefeito19000064976122ABRAAO DAVID NETO53060407805620068773.022PLPartido Liberal1Pessoa Jurídica47318Comércio varejista de combustíveis para veículos automotores40438186000180.0AUTO POSTO NILOPOLIS LTDA MEAUTO POSTO NILOPOLIS LTDA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Recibo420100000Combustíveis e lubrificantes4003634113/10/2020GASOLINA COMUN100.0103
359620202Ordinária426Eleições Municipais 202015/11/20201Final27/01/20211828967198RJNILÓPOLIS3851094100014811Prefeito19000064976122ABRAAO DAVID NETO53060407805620068773.022PLPartido Liberal1Pessoa Jurídica47318Comércio varejista de combustíveis para veículos automotores40438186000180.0AUTO POSTO NILOPOLIS LTDA MEAUTO POSTO NILOPOLIS LTDA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Recibo320100000Combustíveis e lubrificantes4003705513/10/2020GASOLINA COMUN20.094
359220202Ordinária426Eleições Municipais 202015/11/20201Final27/01/20211828967198RJNILÓPOLIS3851094100014811Prefeito19000064976122ABRAAO DAVID NETO53060407805620068773.022PLPartido Liberal1Pessoa Jurídica47318Comércio varejista de combustíveis para veículos automotores40438186000180.0AUTO POSTO NILOPOLIS LTDA MEAUTO POSTO NILOPOLIS LTDA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Recibo220100000Combustíveis e lubrificantes4003685813/10/2020GASOLINA COMUN20.080
502920222Ordinária546Eleições Gerais Estaduais 202202/10/20221Final22/11/20223787879545RJRIO DE JANEIRO475107570001997Deputado Estadual19000162047413321CARLA MARIA MACHADO DOS SANTOS80998828734<NA>13PTPartido dos Trabalhadores1Pessoa Jurídica47318Comércio varejista de combustíveis para veículos automotores28935153000220.0MOTO MERCANTIL CAMPISTA S AMOTO MERCANTIL CAMPISTA S A<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal357020100000Combustíveis e lubrificantes5096102230/09/2022GASOLINA COMUM53.977
293320202Ordinária426Eleições Municipais 202015/11/20201Final15/12/20201841088508RJDUQUE DE CAXIAS3861870200010613Vereador19000073612015320CARLOS AUGUSTO PEREIRA SODRÉ9076966788<NA>15MDBMovimento Democrático Brasileiro1Pessoa Jurídica<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>20210000Encargos financeiros, taxas bancárias e/ou op. cartão de crédito3864294306/11/2020TARIFA BANCÁRIA10.060
358720202Ordinária426Eleições Municipais 202015/11/20201Final27/01/20211828967198RJNILÓPOLIS3851094100014811Prefeito19000064976122ABRAAO DAVID NETO53060407805620068773.022PLPartido Liberal1Pessoa Jurídica47318Comércio varejista de combustíveis para veículos automotores40438186000180.0AUTO POSTO NILOPOLIS LTDA MEAUTO POSTO NILOPOLIS LTDA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Recibo120100000Combustíveis e lubrificantes4003599113/10/2020ETANOL20.060
359420202Ordinária426Eleições Municipais 202015/11/20201Final27/01/20211828967198RJNILÓPOLIS3851094100014811Prefeito19000064976122ABRAAO DAVID NETO53060407805620068773.022PLPartido Liberal1Pessoa Jurídica47318Comércio varejista de combustíveis para veículos automotores40438186000180.0AUTO POSTO NILOPOLIS LTDA MEAUTO POSTO NILOPOLIS LTDA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Recibo320100000Combustíveis e lubrificantes4003705513/10/2020ETANOL20.060
153620182Ordinária297Eleições Gerais Estaduais 201807/10/20181Final21/11/2018427242260RJRIO DE JANEIRO312349400001797Deputado Estadual19000062224312500THIAGO PAMPOLHA GONCALVES11906458740<NA>12PDTPartido Democrático Trabalhista1Pessoa Jurídica18130Impressão de materiais para outros usos10636550000155.0IMPRESSÃO DIGITAL STUDIO X MEIMPRESSAO DIGITAL STUDIO X LTDA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal241 - 120140000Publicidade por materiais impressos2407956701/09/2018CARTÃO VISITA 9 X 5 MILHEIRO THIAGO PAMPOLHA30.059
502520222Ordinária546Eleições Gerais Estaduais 202202/10/20221Final22/11/20223787879545RJRIO DE JANEIRO475107570001997Deputado Estadual19000162047413321CARLA MARIA MACHADO DOS SANTOS80998828734<NA>13PTPartido dos Trabalhadores1Pessoa Jurídica47318Comércio varejista de combustíveis para veículos automotores28935153000220.0MOTO MERCANTIL CAMPISTA S AMOTO MERCANTIL CAMPISTA S A<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Nota Fiscal353320100000Combustíveis e lubrificantes5096088824/09/2022GASOLINA COMUM53.951